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Opatovski N, Nehme E, Zoref N, Barzilai I, Orange Kedem R, Ferdman B, Keselman P, Alalouf O, Shechtman Y. Depth-enhanced high-throughput microscopy by compact PSF engineering. Nat Commun 2024; 15:4861. [PMID: 38849376 PMCID: PMC11161645 DOI: 10.1038/s41467-024-48502-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 05/03/2024] [Indexed: 06/09/2024] Open
Abstract
High-throughput microscopy is vital for screening applications, where three-dimensional (3D) cellular models play a key role. However, due to defocus susceptibility, current 3D high-throughput microscopes require axial scanning, which lowers throughput and increases photobleaching and photodamage. Point spread function (PSF) engineering is an optical method that enables various 3D imaging capabilities, yet it has not been implemented in high-throughput microscopy due to the cumbersome optical extension it typically requires. Here we demonstrate compact PSF engineering in the objective lens, which allows us to enhance the imaging depth of field and, combined with deep learning, recover 3D information using single snapshots. Beyond the applications shown here, this work showcases the usefulness of high-throughput microscopy in obtaining training data for deep learning-based algorithms, applicable to a variety of microscopy modalities.
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Affiliation(s)
- Nadav Opatovski
- Russell Berrie Nanotechnology Institute, Technion - Israel Institute of Technology, Haifa, Israel
| | - Elias Nehme
- Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
- Department of Electrical and Computer Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Noam Zoref
- Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Ilana Barzilai
- Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Reut Orange Kedem
- Russell Berrie Nanotechnology Institute, Technion - Israel Institute of Technology, Haifa, Israel
| | - Boris Ferdman
- Russell Berrie Nanotechnology Institute, Technion - Israel Institute of Technology, Haifa, Israel
| | - Paul Keselman
- Sartorius Stedim North America Inc., Bohemia, NY, USA
| | - Onit Alalouf
- Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Yoav Shechtman
- Russell Berrie Nanotechnology Institute, Technion - Israel Institute of Technology, Haifa, Israel.
- Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel.
- Department of Mechanical Engineering, University of Texas at Austin, Austin, TX, USA.
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2
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Fu B, Brock EE, Andrews R, Breiter JC, Tian R, Toomey CE, Lachica J, Lashley T, Ryten M, Wood NW, Vendruscolo M, Gandhi S, Weiss LE, Beckwith JS, Lee SF. RASP: Optimal Single Puncta Detection in Complex Cellular Backgrounds. J Phys Chem B 2024; 128:3585-3597. [PMID: 38593280 PMCID: PMC11033865 DOI: 10.1021/acs.jpcb.4c00174] [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: 01/09/2024] [Revised: 03/01/2024] [Accepted: 03/25/2024] [Indexed: 04/11/2024]
Abstract
Super-resolution and single-molecule microscopies have been increasingly applied to complex biological systems. A major challenge of these approaches is that fluorescent puncta must be detected in the low signal, high noise, heterogeneous background environments of cells and tissue. We present RASP, Radiality Analysis of Single Puncta, a bioimaging-segmentation method that solves this problem. RASP removes false-positive puncta that other analysis methods detect and detects features over a broad range of spatial scales: from single proteins to complex cell phenotypes. RASP outperforms the state-of-the-art methods in precision and speed using image gradients to separate Gaussian-shaped objects from the background. We demonstrate RASP's power by showing that it can extract spatial correlations between microglia, neurons, and α-synuclein oligomers in the human brain. This sensitive, computationally efficient approach enables fluorescent puncta and cellular features to be distinguished in cellular and tissue environments, with sensitivity down to the level of the single protein. Python and MATLAB codes, enabling users to perform this RASP analysis on their own data, are provided as Supporting Information and links to third-party repositories.
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Affiliation(s)
- Bin Fu
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield
Road, Cambridge CB2 1EW, U.K.
- Aligning
Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815, United States
| | - Emma E. Brock
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield
Road, Cambridge CB2 1EW, U.K.
- Aligning
Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815, United States
| | - Rebecca Andrews
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield
Road, Cambridge CB2 1EW, U.K.
- Aligning
Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815, United States
| | - Jonathan C. Breiter
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield
Road, Cambridge CB2 1EW, U.K.
- Aligning
Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815, United States
- Centre
for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K.
| | - Ru Tian
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield
Road, Cambridge CB2 1EW, U.K.
- Aligning
Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815, United States
- Centre
for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K.
| | - Christina E. Toomey
- Aligning
Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815, United States
- The
Queen Square Brain Bank for Neurological Disorders, Department of
Clinical and Movement Neuroscience, UCL
Queen Square Institute of Neurology, London WC1N 3BG, U.K.
- Department
of Neurodegenerative Diseases, UCL Queen
Square Institute of Neurology, London WC1N 3BG, U.K.
| | - Joanne Lachica
- Aligning
Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815, United States
- The
Queen Square Brain Bank for Neurological Disorders, Department of
Clinical and Movement Neuroscience, UCL
Queen Square Institute of Neurology, London WC1N 3BG, U.K.
- The
Francis Crick Institute, King’s Cross, London NW1 1AT, U.K.
| | - Tammaryn Lashley
- Aligning
Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815, United States
- The
Queen Square Brain Bank for Neurological Disorders, Department of
Clinical and Movement Neuroscience, UCL
Queen Square Institute of Neurology, London WC1N 3BG, U.K.
- Department
of Neurodegenerative Diseases, UCL Queen
Square Institute of Neurology, London WC1N 3BG, U.K.
| | - Mina Ryten
- Aligning
Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815, United States
- Great
Ormond Street Institute of Child Health, University College London, London WC1E 6BT, U.K.
- UK
Dementia Research Institute at the University of Cambridge, Cambridge CB2 0AH, U.K.
- Department
of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0SP, U.K.
| | - Nicholas W. Wood
- Aligning
Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815, United States
- Department
of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London WC1N 3BG, U.K.
| | - Michele Vendruscolo
- Aligning
Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815, United States
- Centre
for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K.
| | - Sonia Gandhi
- Aligning
Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815, United States
- Department
of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London WC1N 3BG, U.K.
- The
Francis Crick Institute, King’s Cross, London NW1 1AT, U.K.
| | - Lucien E. Weiss
- Department of Engineering Physics, Polytechnique
Montréal, Montréal, Québec H3T 1J4, Canada
| | - Joseph S. Beckwith
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield
Road, Cambridge CB2 1EW, U.K.
- Aligning
Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815, United States
| | - Steven F. Lee
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield
Road, Cambridge CB2 1EW, U.K.
- Aligning
Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815, United States
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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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Gupta R, Singh M, Pathania R. Chemical genetic approaches for the discovery of bacterial cell wall inhibitors. RSC Med Chem 2023; 14:2125-2154. [PMID: 37974958 PMCID: PMC10650376 DOI: 10.1039/d3md00143a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 08/10/2023] [Indexed: 11/19/2023] Open
Abstract
Antimicrobial resistance (AMR) in bacterial pathogens is a worldwide health issue. The innovation gap in discovering new antibiotics has remained a significant hurdle in combating the AMR problem. Currently, antibiotics target various vital components of the bacterial cell envelope, nucleic acid and protein biosynthesis machinery and metabolic pathways essential for bacterial survival. The critical role of the bacterial cell envelope in cell morphogenesis and integrity makes it an attractive drug target. While a significant number of in-clinic antibiotics target peptidoglycan biosynthesis, several components of the bacterial cell envelope have been overlooked. This review focuses on various antibacterial targets in the bacterial cell wall and the strategies employed to find their novel inhibitors. This review will further elaborate on combining forward and reverse chemical genetic approaches to discover antibacterials that target the bacterial cell envelope.
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Affiliation(s)
- Rinki Gupta
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee Roorkee - 247 667 Uttarakhand India
| | - Mangal Singh
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee Roorkee - 247 667 Uttarakhand India
| | - Ranjana Pathania
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee Roorkee - 247 667 Uttarakhand India
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5
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Stossi F, Singh PK, Safari K, Marini M, Labate D, Mancini MA. High throughput microscopy and single cell phenotypic image-based analysis in toxicology and drug discovery. Biochem Pharmacol 2023; 216:115770. [PMID: 37660829 DOI: 10.1016/j.bcp.2023.115770] [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: 06/01/2023] [Revised: 08/23/2023] [Accepted: 08/25/2023] [Indexed: 09/05/2023]
Abstract
Measuring single cell responses to the universe of chemicals (drugs, natural products, environmental toxicants etc.) is of paramount importance to human health as phenotypic variability in sensing stimuli is a hallmark of biology that is considered during high throughput screening. One of the ways to approach this problem is via high throughput, microscopy-based assays coupled with multi-dimensional single cell analysis methods. Here, we will summarize some of the efforts in this vast and growing field, focusing on phenotypic screens (e.g., Cell Painting), single cell analytics and quality control, with particular attention to environmental toxicology and drug screening. We will discuss advantages and limitations of high throughput assays with various end points and levels of complexity.
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Affiliation(s)
- Fabio Stossi
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA; GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA.
| | - Pankaj K Singh
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Kazem Safari
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Michela Marini
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Department of Mathematics, University of Houston, Houston, TX, USA
| | - Demetrio Labate
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Department of Mathematics, University of Houston, Houston, TX, USA
| | - Michael A Mancini
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA; GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
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6
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Rathore D, Marino MJ, Nita-Lazar A. Omics and systems view of innate immune pathways. Proteomics 2023; 23:e2200407. [PMID: 37269203 DOI: 10.1002/pmic.202200407] [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: 02/14/2023] [Revised: 04/16/2023] [Accepted: 05/23/2023] [Indexed: 06/04/2023]
Abstract
Multiomics approaches to studying systems biology are very powerful techniques that can elucidate changes in the genomic, transcriptomic, proteomic, and metabolomic levels within a cell type in response to an infection. These approaches are valuable for understanding the mechanisms behind disease pathogenesis and how the immune system responds to being challenged. With the emergence of the COVID-19 pandemic, the importance and utility of these tools have become evident in garnering a better understanding of the systems biology within the innate and adaptive immune response and for developing treatments and preventative measures for new and emerging pathogens that pose a threat to human health. In this review, we focus on state-of-the-art omics technologies within the scope of innate immunity.
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Affiliation(s)
- Deepali Rathore
- Functional Cellular Networks Section, Laboratory of Immune Systems Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Matthew J Marino
- Functional Cellular Networks Section, Laboratory of Immune Systems Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Aleksandra Nita-Lazar
- Functional Cellular Networks Section, Laboratory of Immune Systems Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
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7
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Wang Y, Yin N, Yang R, Faiola F. Pollution effects on retinal health: A review on current methodologies and findings. Toxicol Ind Health 2023; 39:336-344. [PMID: 37160417 DOI: 10.1177/07482337231174072] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
In our daily life, we are exposed to numerous industrial chemicals that may be harmful to the retina, which is a delicate and sensitive part of our eyes. This could lead to irreversible changes and cause retinal diseases or blindness. Current retinal environmental health studies primarily utilize animal models, isolated mammalian retinas, animal- or human-derived retinal cells, and retinal organoids, to address both pre- and postnatal exposure. However, as there is limited toxicological information available for specific populations, human induced pluripotent stem cell (hiPSC)-induced models could be effective tools to supplement such data. In order to obtain more comprehensive and reliable toxicological information, we need more appropriate models, novel evaluation methods, and computational technologies to develop portable equipment. This review mainly focused on current toxicology models with particular emphasis on retinal organoids, and it looks forward to future models, analytical methods, and equipment that can efficiently and accurately evaluate retinal toxicity.
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Affiliation(s)
- Yue Wang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Nuoya Yin
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Renjun Yang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Francesco Faiola
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
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8
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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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9
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Field-dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging. Nat Methods 2023; 20:459-468. [PMID: 36823335 DOI: 10.1038/s41592-023-01775-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 01/09/2023] [Indexed: 02/25/2023]
Abstract
Single-molecule localization microscopy in a typical wide-field setup has been widely used for investigating subcellular structures with super resolution; however, field-dependent aberrations restrict the field of view (FOV) to only tens of micrometers. Here, we present a deep-learning method for precise localization of spatially variant point emitters (FD-DeepLoc) over a large FOV covering the full chip of a modern sCMOS camera. Using a graphic processing unit-based vectorial point spread function (PSF) fitter, we can fast and accurately model the spatially variant PSF of a high numerical aperture objective in the entire FOV. Combined with deformable mirror-based optimal PSF engineering, we demonstrate high-accuracy three-dimensional single-molecule localization microscopy over a volume of ~180 × 180 × 5 μm3, allowing us to image mitochondria and nuclear pore complexes in entire cells in a single imaging cycle without hardware scanning; a 100-fold increase in throughput compared to the state of the art.
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10
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Azeroglu B, Ozbun L, Pegoraro G, Lazzerini Denchi E. Native FISH: A low- and high-throughput assay to analyze the alternative lengthening of telomere (ALT) pathway. Methods Cell Biol 2022; 182:265-284. [PMID: 38359982 DOI: 10.1016/bs.mcb.2022.10.010] [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] [Indexed: 11/29/2022]
Abstract
Alternative lengthening of telomeres (ALT) is a telomerase-independent and recombination-based mechanism used by approximately 15% of human cancers to maintain telomere length and to sustain proliferation. ALT-positive cells display unique features that could be exploited for tailored cancer therapies. A key limitation for the development of ALT-specific treatments is the lack of an assay to detect ALT-positive cells that is easy to perform and that can be scaled up. One of the most broadly used assays for ALT detection, CCA (C-circle assay), does not provide single-cell information and it is not amenable to High-Throughput Screening (HTS). To overcome these limitations, we developed Native-FISH (N-FISH) as an alternative method to visualize ALT-specific single-stranded telomeric DNA. N-FISH produces single-cell data, can be applied to fixed tissues, does not require DNA isolation or amplification steps, and it can be miniaturized in a 384-well format. This protocol details the steps to perform N-FISH protocol both in a low- and high-throughput format to analyze ALT. While low-throughput N-FISH is useful to assay the ALT state of cell lines, we expect that the miniaturized N-FISH assay coupled with high-throughput imaging will be useful in functional genomics and chemical screens to identify novel cellular factors that regulate ALT and potential ALT therapeutic targets for cancer therapies directed against ALT-positive tumors, respectively.
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Affiliation(s)
- Benura Azeroglu
- Laboratory of Genome Integrity, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, United States.
| | - Laurent Ozbun
- High-Throughput Imaging Facility (HiTIF), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Gianluca Pegoraro
- High-Throughput Imaging Facility (HiTIF), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Eros Lazzerini Denchi
- Laboratory of Genome Integrity, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, United States
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11
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Winkler S, Menke J, Meyer KV, Kortmann C, Bahnemann J. Automation of cell culture assays using a 3D-printed servomotor-controlled microfluidic valve system. LAB ON A CHIP 2022; 22:4656-4665. [PMID: 36342331 DOI: 10.1039/d2lc00629d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Microfluidic valve systems show great potential to automate mixing, dilution, and time-resolved reagent supply within biochemical assays and novel on-chip cell culture systems. However, most of these systems require a complex and cost-intensive fabrication in clean room facilities, and the valve control element itself also requires vacuum or pressure sources (including external valves, tubing, ports and pneumatic control channels). Addressing these bottlenecks, the herein presented biocompatible and heat steam sterilizable microfluidic valve system was fabricated via high-resolution 3D printing in a one-step process - including inlets, micromixer, microvalves, and outlets. The 3D-printed valve membrane is deflected via miniature on-chip servomotors that are controlled using a Raspberry Pi and a customized Python script (resulting in a device that is comparatively low-cost, portable, and fully automated). While a high mixing accuracy and long-term robustness is established, as described herein the system is further applied in a proof-of-concept assay for automated IC50 determination of camptothecin with mouse fibroblasts (L929) monitored by a live-cell-imaging system. Measurements of cell growth and IC50 values revealed no difference in performance between the microfluidic valve system and traditional pipetting. This novel design and the accompanying automatization scripts provide the scientific community with direct access to customizable full-time reagent control of 2D cell culture, or even novel organ-on-a-chip systems.
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Affiliation(s)
- Steffen Winkler
- Institute of Technical Chemistry, Leibniz University Hannover, Hannover, Germany
| | - Jannik Menke
- Institute of Technical Chemistry, Leibniz University Hannover, Hannover, Germany
| | - Katharina V Meyer
- Institute of Technical Chemistry, Leibniz University Hannover, Hannover, Germany
| | - Carlotta Kortmann
- Institute of Technical Chemistry, Leibniz University Hannover, Hannover, Germany
| | - Janina Bahnemann
- Institute of Physics, University of Augsburg, Universitätsstraße 1, 86159 Augsburg, Germany.
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12
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Pooled image-base screening of mitochondria with microraft isolation distinguishes pathogenic mitofusin 2 mutations. Commun Biol 2022; 5:1128. [PMID: 36284160 PMCID: PMC9596453 DOI: 10.1038/s42003-022-04089-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 10/11/2022] [Indexed: 11/08/2022] Open
Abstract
Most human genetic variation is classified as variants of uncertain significance. While advances in genome editing have allowed innovation in pooled screening platforms, many screens deal with relatively simple readouts (viability, fluorescence) and cannot identify the complex cellular phenotypes that underlie most human diseases. In this paper, we present a generalizable functional genomics platform that combines high-content imaging, machine learning, and microraft isolation in a method termed “Raft-Seq”. We highlight the efficacy of our platform by showing its ability to distinguish pathogenic point mutations of the mitochondrial regulator Mitofusin 2, even when the cellular phenotype is subtle. We also show that our platform achieves its efficacy using multiple cellular features, which can be configured on-the-fly. Raft-Seq enables a way to perform pooled screening on sets of mutations in biologically relevant cells, with the ability to physically capture any cell with a perturbed phenotype and expand it clonally, directly from the primary screen. Raft-Seq is a generalizable pooled screening platform that combines high-content imaging, machine learning and microraft isolation, and enables efficient screening of genetic perturbations based on their impact on phenotypes.
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13
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Shilo A, Pegoraro G, Misteli T. HiFENS: high-throughput FISH detection of endogenous pre-mRNA splicing isoforms. Nucleic Acids Res 2022; 50:e130. [PMID: 36243969 PMCID: PMC9825148 DOI: 10.1093/nar/gkac869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 09/01/2022] [Accepted: 09/27/2022] [Indexed: 01/29/2023] Open
Abstract
Splicing factors play an essential role in regulation of alternative pre-mRNA splicing. While much progress has been made in delineating the mechanisms of the splicing machinery, the identity of signal transduction pathways and upstream factors that regulate splicing factor activity is largely unknown. A major challenge in the discovery of upstream regulatory factors of pre-mRNA splicing is the scarcity of functional genomics screening methods to monitor splicing outcomes of endogenous genes. Here, we have developed HiFENS (high throughput FISH detection of endogenous splicing isoforms), a high-throughput imaging assay based on hybridization chain reaction (HCR) and used HiFENS to screen for cellular factors that regulate alternative splicing of endogenous genes. We demonstrate optimized detection with high specificity of endogenous splicing isoforms and multiplexing of probes for accurate detection of splicing outcomes with single cell resolution. As proof-of-principle, we perform an RNAi screen of 702 human kinases and identify potential candidate upstream splicing regulators of the FGFR2 gene. HiFENS should be a useful tool for the unbiased delineation of cellular pathways involved in alternative splicing regulation.
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Affiliation(s)
- Asaf Shilo
- Cell Biology of Genomes, Center for Cancer Research (CCR), National Cancer Institute, NIH, Bethesda, MD 20892, USA
| | - Gianluca Pegoraro
- High-Throughput Imaging Facility (HiTIF), Center for Cancer Research (CCR), National Cancer Institute, NIH, Bethesda, MD 20892, USA
| | - Tom Misteli
- To whom correspondence should be addressed. Tel: +1 240 670 6669; Fax: +1 240 670 6670;
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14
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Fang L, Li D, Yin J, Pan H, Ye H, Bowman J, Capaldo B, Kelly K. TMPRSS2-ERG promotes the initiation of prostate cancer by suppressing oncogene-induced senescence. Cancer Gene Ther 2022; 29:1463-1476. [PMID: 35393570 PMCID: PMC9537368 DOI: 10.1038/s41417-022-00454-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 02/16/2022] [Accepted: 03/04/2022] [Indexed: 11/25/2022]
Abstract
ERG translocations are commonly involved in the initiation of prostate neoplasia, yet previous experimental approaches have not addressed mechanisms of oncogenic inception. Here, in a genetically engineered mouse model, combining TMPRSS2-driven ERG with KrasG12D led to invasive prostate adenocarcinomas, while ERG or KrasG12D alone were non-oncogenic. In primary prostate luminal epithelial cells, following inducible oncogenic Kras expression or Pten depletion, TMPRSS2-ERG suppressed oncogene-induced senescence, independent of TP53 induction and RB1 inhibition. Oncogenic KRAS and TMPRSS2-ERG synergized to promote tumorigenesis and metastasis of primary luminal cells. The presence of TMPRSS2-ERG compared to a wild-type background was associated with a stemness phenotype and with relatively increased RAS-induced differential gene expression for MYC and mTOR-regulated pathways, including protein translation and lipogenesis. In addition, mTOR inhibitors abrogated ERG-dependent senescence resistance. These studies reveal a previously unappreciated function whereby ERG expression primes preneoplastic cells for the accumulation of additional gene mutations by suppression of oncogene-induced senescence.
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Affiliation(s)
- Lei Fang
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, Bethesda, MD, USA
| | - Dongmei Li
- Immunology and Reproduction Biology Laboratory & State Key Laboratory of Analytical Chemistry for Life Science, Medical School, Nanjing University, Nanjing, Jiangsu, P. R. China
| | - JuanJuan Yin
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, Bethesda, MD, USA
| | - Hong Pan
- Department of Oncology, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang, P. R. China
| | - Huihui Ye
- Department of Pathology and Department of Urology, University of California Los Angeles, Los Angeles, CA, USA
| | - Joel Bowman
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, Bethesda, MD, USA
| | - Brian Capaldo
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, Bethesda, MD, USA
| | - Kathleen Kelly
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, Bethesda, MD, USA.
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15
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Gadkari M, Sun J, Carcamo A, Alessi H, Hu Z, Fraser IDC, Pegoraro G, Franco LM. High-throughput imaging of mRNA at the single-cell level in human primary immune cells. RNA (NEW YORK, N.Y.) 2022; 28:1263-1278. [PMID: 35764396 PMCID: PMC9380748 DOI: 10.1261/rna.079239.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 06/06/2022] [Indexed: 06/15/2023]
Abstract
Measurement of gene expression at the single-cell level has advanced the study of transcriptional regulation programs in healthy and disease states. In particular, single-cell approaches have shed light on the high level of transcriptional heterogeneity of individual cells, both at baseline and in response to experimental or environmental perturbations. We have developed a method for high-content imaging (HCI)-based quantification of relative changes in transcript abundance at the single-cell level in human primary immune cells and have validated its performance under multiple experimental conditions to demonstrate its general applicability. This method, named hcHCR, combines the sensitivity of the hybridization chain reaction (HCR) for the visualization of RNA in single cells, with the speed, scalability, and reproducibility of HCI. We first tested eight cell attachment substrates for short-term culture of primary human B cells, T cells, monocytes, or neutrophils. We then miniaturized HCR in 384-well format and documented the ability of the method to detect changes in transcript abundance at the single-cell level in thousands of cells for each experimental condition by HCI. Furthermore, we demonstrated the feasibility of multiplexing gene expression measurements by simultaneously assaying the abundance of three transcripts per cell at baseline and in response to an experimental stimulus. Finally, we tested the robustness of the assay to technical and biological variation. We anticipate that hcHCR will be suitable for low- to medium-throughput chemical or functional genomics screens in primary human cells, with the possibility of performing screens on cells obtained from patients with a specific disease.
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Affiliation(s)
- Manasi Gadkari
- Functional Immunogenomics Section, Systemic Autoimmunity Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Jing Sun
- Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Adrian Carcamo
- High-Throughput Imaging Facility (HiTIF), National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Hugh Alessi
- Functional Immunogenomics Section, Systemic Autoimmunity Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Zonghui Hu
- Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Maryland 20852, USA
| | - Iain D C Fraser
- Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Gianluca Pegoraro
- High-Throughput Imaging Facility (HiTIF), National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Luis M Franco
- Functional Immunogenomics Section, Systemic Autoimmunity Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, Maryland 20892, USA
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16
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Choudhary K, Pico AR. Introducing R as a smart version of calculators enables beginners to explore it on their own. F1000Res 2022; 10:859. [PMID: 35399224 PMCID: PMC8976183 DOI: 10.12688/f1000research.54685.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/17/2022] [Indexed: 11/20/2022] Open
Abstract
Rapid technological advances in the past decades have enabled molecular biologists to generate large-scale and complex data with affordable resource investments, or obtain such data from public repositories. Yet, many graduate students, postdoctoral scholars, and senior researchers in the biosciences find themselves ill-equipped to analyze large-scale data. Global surveys have revealed that active researchers prefer short training workshops to fill their skill gaps. In this article, we focus on the challenge of delivering a short data analysis workshop to absolute beginners in computer programming. We propose that introducing R or other programming languages for data analysis as smart versions of calculators can help lower the communication barrier with absolute beginners. We describe this comparison with a few analogies and hope that other instructors will find them useful. We utilized these in our four-hour long training workshops involving participatory live coding, which we delivered in person and via videoconferencing. Anecdotal evidence suggests that our exposition made R programming seem easy and enabled beginners to explore it on their own.
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Affiliation(s)
- Krishna Choudhary
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, 94158, USA
- Diabetes Center, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Alexander R. Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, 94158, USA
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17
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Betge J, Rindtorff N, Sauer J, Rauscher B, Dingert C, Gaitantzi H, Herweck F, Srour-Mhanna K, Miersch T, Valentini E, Boonekamp KE, Hauber V, Gutting T, Frank L, Belle S, Gaiser T, Buchholz I, Jesenofsky R, Härtel N, Zhan T, Fischer B, Breitkopf-Heinlein K, Burgermeister E, Ebert MP, Boutros M. The drug-induced phenotypic landscape of colorectal cancer organoids. Nat Commun 2022; 13:3135. [PMID: 35668108 PMCID: PMC9170716 DOI: 10.1038/s41467-022-30722-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 05/12/2022] [Indexed: 12/14/2022] Open
Abstract
Patient-derived organoids resemble the biology of tissues and tumors, enabling ex vivo modeling of human diseases. They have heterogeneous morphologies with unclear biological causes and relationship to treatment response. Here, we use high-throughput, image-based profiling to quantify phenotypes of over 5 million individual colorectal cancer organoids after treatment with >500 small molecules. Integration of data using multi-omics modeling identifies axes of morphological variation across organoids: Organoid size is linked to IGF1 receptor signaling, and cystic vs. solid organoid architecture is associated with LGR5 + stemness. Treatment-induced organoid morphology reflects organoid viability, drug mechanism of action, and is biologically interpretable. Inhibition of MEK leads to cystic reorganization of organoids and increases expression of LGR5, while inhibition of mTOR induces IGF1 receptor signaling. In conclusion, we identify shared axes of variation for colorectal cancer organoid morphology, their underlying biological mechanisms, and pharmacological interventions with the ability to move organoids along them. The heterogeneity underlying cancer organoid phenotypes is not yet well understood. Here, the authors develop an imaging analysis assay for high throughput phenotypic screening of colorectal organoids that allows to define specific morphological changes that occur following different drug treatments.
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Affiliation(s)
- Johannes Betge
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, and Heidelberg University, Medical Faculty Mannheim, Department of Cell and Molecular Biology, Heidelberg, Germany.,Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,German Cancer Research Center (DKFZ), Junior Clinical Cooperation Unit Translational Gastrointestinal Oncology and Preclinical Models, Heidelberg, Germany.,DKFZ-Hector Cancer Institute at University Medical Center Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany
| | - Niklas Rindtorff
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, and Heidelberg University, Medical Faculty Mannheim, Department of Cell and Molecular Biology, Heidelberg, Germany
| | - Jan Sauer
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, and Heidelberg University, Medical Faculty Mannheim, Department of Cell and Molecular Biology, Heidelberg, Germany.,German Cancer Research Center (DKFZ), Computational Genome Biology Group, Heidelberg, Germany
| | - Benedikt Rauscher
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, and Heidelberg University, Medical Faculty Mannheim, Department of Cell and Molecular Biology, Heidelberg, Germany
| | - Clara Dingert
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, and Heidelberg University, Medical Faculty Mannheim, Department of Cell and Molecular Biology, Heidelberg, Germany
| | - Haristi Gaitantzi
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany
| | - Frank Herweck
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany
| | - Kauthar Srour-Mhanna
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,German Cancer Research Center (DKFZ), Junior Clinical Cooperation Unit Translational Gastrointestinal Oncology and Preclinical Models, Heidelberg, Germany.,DKFZ-Hector Cancer Institute at University Medical Center Mannheim, Mannheim, Germany
| | - Thilo Miersch
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, and Heidelberg University, Medical Faculty Mannheim, Department of Cell and Molecular Biology, Heidelberg, Germany
| | - Erica Valentini
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, and Heidelberg University, Medical Faculty Mannheim, Department of Cell and Molecular Biology, Heidelberg, Germany
| | - Kim E Boonekamp
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, and Heidelberg University, Medical Faculty Mannheim, Department of Cell and Molecular Biology, Heidelberg, Germany
| | - Veronika Hauber
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany
| | - Tobias Gutting
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany.,Department of Internal Medicine IV, Heidelberg University, Heidelberg, Germany
| | - Larissa Frank
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, and Heidelberg University, Medical Faculty Mannheim, Department of Cell and Molecular Biology, Heidelberg, Germany
| | - Sebastian Belle
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany
| | - Timo Gaiser
- Mannheim Cancer Center, Mannheim, Germany.,Heidelberg University, Institute of Pathology, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany
| | - Inga Buchholz
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany
| | - Ralf Jesenofsky
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany
| | - Nicolai Härtel
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany
| | - Tianzuo Zhan
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, and Heidelberg University, Medical Faculty Mannheim, Department of Cell and Molecular Biology, Heidelberg, Germany.,Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany
| | - Bernd Fischer
- German Cancer Research Center (DKFZ), Computational Genome Biology Group, Heidelberg, Germany
| | - Katja Breitkopf-Heinlein
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany
| | - Elke Burgermeister
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany
| | - Matthias P Ebert
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany. .,DKFZ-Hector Cancer Institute at University Medical Center Mannheim, Mannheim, Germany. .,Mannheim Cancer Center, Mannheim, Germany.
| | - Michael Boutros
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, and Heidelberg University, Medical Faculty Mannheim, Department of Cell and Molecular Biology, Heidelberg, Germany. .,German Cancer Consortium (DKTK), Heidelberg, Germany.
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18
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Rezvani A, Bigverdi M, Rohban MH. Image-based cell profiling enhancement via data cleaning methods. PLoS One 2022; 17:e0267280. [PMID: 35507559 PMCID: PMC9067647 DOI: 10.1371/journal.pone.0267280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 04/06/2022] [Indexed: 11/18/2022] Open
Abstract
With the advent of high-throughput assays, a large number of biological experiments can be carried out. Image-based assays are among the most accessible and inexpensive technologies for this purpose. Indeed, these assays have proved to be effective in characterizing unknown functions of genes and small molecules. Image analysis pipelines have a pivotal role in translating raw images that are captured in such assays into useful and compact representation, also known as measurements. CellProfiler is a popular and commonly used tool for this purpose through providing readily available modules for the cell/nuclei segmentation, and making various measurements, or features, for each cell/nuclei. Single cell features are then aggregated for each treatment replica to form treatment “profiles”. However, there may be several sources of error in the CellProfiler quantification pipeline that affects the downstream analysis that is performed on the profiles. In this work, we examined various preprocessing approaches to improve the profiles. We consider the identification of drug mechanisms of action as the downstream task to evaluate such preprocessing approaches. Our enhancement steps mainly consist of data cleaning, cell level outlier detection, toxic drug detection, and regressing out the cell area from all other features, as many of them are widely affected by the cell area. Our experiments indicate that by performing these time-efficient preprocessing steps, image-based profiles can preserve more meaningful information compared to raw profiles. In the end, we also suggest possible avenues for future research.
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Affiliation(s)
- Arghavan Rezvani
- Department of Computer Engineering, Sharif University of Technology, Tehran, Tehran, Iran
| | - Mahtab Bigverdi
- Department of Computer Engineering, Sharif University of Technology, Tehran, Tehran, Iran
| | - Mohammad Hossein Rohban
- Department of Computer Engineering, Sharif University of Technology, Tehran, Tehran, Iran
- * E-mail:
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19
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HiIDDD: a high-throughput imaging pipeline for the quantitative detection of DNA damage in primary human immune cells. Sci Rep 2022; 12:6335. [PMID: 35428779 PMCID: PMC9022135 DOI: 10.1038/s41598-022-10018-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 03/25/2022] [Indexed: 11/21/2022] Open
Abstract
DNA damage is a prominent biomarker for numerous diseases, including cancer, as well as for the aging process. Detection of DNA damage routinely relies on traditional microscopy or cytometric methods. However, these techniques are typically of limited throughput and are not ideally suited for large-scale longitudinal and population studies that require analysis of large sample sets. We have developed HiIDDD (High-throughput Immune cell DNA Damage Detection), a robust, quantitative and single-cell assay that measures DNA damage by high-throughput imaging using the two major DNA damage markers 53BP1 and \documentclass[12pt]{minimal}
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\begin{document}$$\upgamma$$\end{document}γ-H2AX. We demonstrate sensitive detection with low inter-assay variability of DNA damage in various types of freshly isolated and cryopreserved primary human immune cells, including CD4 + and CD8 + T cells, B cells and monocytes. As proof of principle, we demonstrate parallel batch processing of several immune cell types from multiple donors. We find common patterns of DNA damage in multiple immune cell types of donors of varying ages, suggesting that immune cell properties are specific to individuals. These results establish a novel high-throughput assay for the evaluation of DNA damage in large-scale studies.
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20
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Otterstrom JJ, Lubin A, Payne EM, Paran Y. Technologies bringing young Zebrafish from a niche field to the limelight. SLAS Technol 2022; 27:109-120. [PMID: 35058207 DOI: 10.1016/j.slast.2021.12.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Fundamental life science and pharmaceutical research are continually striving to provide physiologically relevant context for their biological studies. Zebrafish present an opportunity for high-content screening (HCS) to bring a true in vivo model system to screening studies. Zebrafish embryos and young larvae are an economical, human-relevant model organism that are amenable to both genetic engineering and modification, and direct inspection via microscopy. The use of these organisms entails unique challenges that new technologies are overcoming, including artificial intelligence (AI). In this perspective article, we describe the state-of-the-art in terms of automated sample handling, imaging, and data analysis with zebrafish during early developmental stages. We highlight advances in orienting the embryos, including the use of robots, microfluidics, and creative multi-well plate solutions. Analyzing the micrographs in a fast, reliable fashion that maintains the anatomical context of the fluorescently labeled cells is a crucial step. Existing software solutions range from AI-driven commercial solutions to bespoke analysis algorithms. Deep learning appears to be a critical tool that researchers are only beginning to apply, but already facilitates many automated steps in the experimental workflow. Currently, such work has permitted the cellular quantification of multiple cell types in vivo, including stem cell responses to stress and drugs, neuronal myelination and macrophage behavior during inflammation and infection. We evaluate pro and cons of proprietary versus open-source methodologies for combining technologies into fully automated workflows of zebrafish studies. Zebrafish are poised to charge into HCS with ever-greater presence, bringing a new level of physiological context.
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Affiliation(s)
| | - Alexandra Lubin
- Research Department of Hematology, Cancer Institute, University College London, London, UK
| | - Elspeth M Payne
- Research Department of Hematology, Cancer Institute, University College London, London, UK
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21
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Watson ER, Taherian Fard A, Mar JC. Computational Methods for Single-Cell Imaging and Omics Data Integration. Front Mol Biosci 2022; 8:768106. [PMID: 35111809 PMCID: PMC8801747 DOI: 10.3389/fmolb.2021.768106] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 11/29/2021] [Indexed: 12/12/2022] Open
Abstract
Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile at the cellular level. Although the use of imaging data is well established in biomedical research, its primary application has been to observe phenotypes at the tissue or organ level, often using medical imaging techniques such as MRI, CT, and PET. These imaging technologies complement omics-based data in biomedical research because they are helpful for identifying associations between genotype and phenotype, along with functional changes occurring at the tissue level. Single cell imaging can act as an intermediary between these levels. Meanwhile new technologies continue to arrive that can be used to interrogate the genome of single cells and its related omics datasets. As these two areas, single cell imaging and single cell omics, each advance independently with the development of novel techniques, the opportunity to integrate these data types becomes more and more attractive. This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomena like ageing. We include an emphasis on machine learning algorithms because of their ability to identify complex patterns in large multidimensional data.
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Affiliation(s)
| | - Atefeh Taherian Fard
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia
| | - Jessica Cara Mar
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia
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22
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Alacid E, Richards TA. A cell-cell atlas approach for understanding symbiotic interactions between microbes. Curr Opin Microbiol 2021; 64:47-59. [PMID: 34655935 DOI: 10.1016/j.mib.2021.09.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 08/23/2021] [Accepted: 09/01/2021] [Indexed: 01/04/2023]
Abstract
Natural environments are composed of a huge diversity of microorganisms interacting with each other to form complex functional networks. Our understanding of the operative nature of host-symbiont associations is limited because propagating such associations in a laboratory is challenging. The advent of single-cell technologies applied to, for example, animal cells and apicomplexan parasites has revolutionized our understanding of development and disease. Such cell atlas approaches generate maps of cell-specific processes and variations within cellular populations. These methods can now be combined with cellular-imaging so that interaction stage versus transcriptome state can be quantized for microbe-microbe interactions. We predict that the combination of these methods applied to the study of symbioses will transform our understanding of many ecological interactions, including those sampled directly from natural environments.
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Affiliation(s)
- Elisabet Alacid
- Department of Zoology, University of Oxford, 11a Mansfield Road, Oxford OX1 3SZ, UK.
| | - Thomas A Richards
- Department of Zoology, University of Oxford, 11a Mansfield Road, Oxford OX1 3SZ, UK.
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23
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Renner H, Schöler HR, Bruder JM. Combining Automated Organoid Workflows With Artificial Intelligence-Based Analyses: Opportunities to Build a New Generation of Interdisciplinary High-Throughput Screens for Parkinson's Disease and Beyond. Mov Disord 2021; 36:2745-2762. [PMID: 34498298 DOI: 10.1002/mds.28775] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 08/05/2021] [Accepted: 08/09/2021] [Indexed: 12/14/2022] Open
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disease and primarily characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta of the midbrain. Despite decades of research and the development of various disease model systems, there is no curative treatment. This could be due to current model systems, including cell culture and animal models, not adequately recapitulating human PD etiology. More complex human disease models, including human midbrain organoids, are maturing technologies that increasingly enable the strategic incorporation of the missing components needed to model PD in vitro. The resulting organoid-based biological complexity provides new opportunities and challenges in data analysis of rich multimodal data sets. Emerging artificial intelligence (AI) capabilities can take advantage of large, broad data sets and even correlate results across disciplines. Current organoid technologies no longer lack the prerequisites for large-scale high-throughput screening (HTS) and can generate complex yet reproducible data suitable for AI-based data mining. We have recently developed a fully scalable and HTS-compatible workflow for the generation, maintenance, and analysis of three-dimensional (3D) microtissues mimicking key characteristics of the human midbrain (called "automated midbrain organoids," AMOs). AMOs build a reproducible, scalable foundation for creating next-generation 3D models of human neural disease that can fuel mechanism-agnostic phenotypic drug discovery in human in vitro PD models and beyond. Here, we explore the opportunities and challenges resulting from the convergence of organoid HTS and AI-driven data analytics and outline potential future avenues toward the discovery of novel mechanisms and drugs in PD research. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Henrik Renner
- Department of Cell and Developmental Biology, Max Planck Institute for Molecular Biomedicine, Münster, Germany
| | - Hans R Schöler
- Department of Cell and Developmental Biology, Max Planck Institute for Molecular Biomedicine, Münster, Germany
| | - Jan M Bruder
- Department of Cell and Developmental Biology, Max Planck Institute for Molecular Biomedicine, Münster, Germany
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24
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Ren J, Han KY. 2.5D microscopy with polarization independent SLM for enhanced detection efficiency and aberration correction. OPTICS EXPRESS 2021; 29:27530-27541. [PMID: 34615167 PMCID: PMC8687110 DOI: 10.1364/oe.434260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 07/30/2021] [Accepted: 08/03/2021] [Indexed: 06/13/2023]
Abstract
Fast, volumetric imaging by fluorescence microscopy is essential in studying biological phenomena and cellular functions. Recently, single-shot 2.5D microscopy showed promising results for high-throughput quantitative subcellular analysis via extended depth of field imaging without sequential z-scanning; however, the detection efficiency was limited and it lacked depth-induced aberration correction. Here we report that a spatial light modulator (SLM) in a polarization insensitive configuration can significantly improve the detection efficiency of 2.5D microscopy, while also compensating for aberrations at large imaging depths caused by the refractive index mismatch between the sample and the immersion medium. We highlight the improved efficiency via quantitative single-molecule RNA imaging of mammalian cells with a 2-fold improvement in the fluorescence intensity compared to a conventional SLM-based microscopy. We demonstrate the aberration correction capabilities and extended depth of field by imaging thick specimens with fewer z-scanning steps.
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25
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Lai QTK, Yip GGK, Wu J, Wong JSJ, Lo MCK, Lee KCM, Le TTHD, So HKH, Ji N, Tsia KK. High-speed laser-scanning biological microscopy using FACED. Nat Protoc 2021; 16:4227-4264. [PMID: 34341580 DOI: 10.1038/s41596-021-00576-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 05/25/2021] [Indexed: 12/28/2022]
Abstract
Laser scanning is used in advanced biological microscopy to deliver superior imaging contrast, resolution and sensitivity. However, it is challenging to scale up the scanning speed required for interrogating a large and heterogeneous population of biological specimens or capturing highly dynamic biological processes at high spatiotemporal resolution. Bypassing the speed limitation of traditional mechanical methods, free-space angular-chirp-enhanced delay (FACED) is an all-optical, passive and reconfigurable laser-scanning approach that has been successfully applied in different microscopy modalities at an ultrafast line-scan rate of 1-80 MHz. Optimal FACED imaging performance requires optimized experimental design and implementation to enable specific high-speed applications. In this protocol, we aim to disseminate information allowing FACED to be applied to a broader range of imaging modalities. We provide (i) a comprehensive guide and design specifications for the FACED hardware; (ii) step-by-step optical implementations of the FACED module including the key custom components; and (iii) the overall image acquisition and reconstruction pipeline. We illustrate two practical imaging configurations: multimodal FACED imaging flow cytometry (bright-field, fluorescence and second-harmonic generation) and kHz 2D two-photon fluorescence microscopy. Users with basic experience in optical microscope operation and software engineering should be able to complete the setup of the FACED imaging hardware and software in ~2-3 months.
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Affiliation(s)
- Queenie T K Lai
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Gwinky G K Yip
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Jianglai Wu
- Department of Physics, University of California, Berkeley, Berkeley, CA, USA.,Chinese Institute for Brain Research, Beijing, China
| | - Justin S J Wong
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Michelle C K Lo
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Kelvin C M Lee
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Tony T H D Le
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Hayden K H So
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Na Ji
- Department of Physics, University of California, Berkeley, Berkeley, CA, USA. .,Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA. .,Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA. .,Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
| | - Kevin K Tsia
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China. .,Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin New Town, Hong Kong.
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26
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Fisch D, Evans R, Clough B, Byrne SK, Channell WM, Dockterman J, Frickel EM. HRMAn 2.0: Next-generation artificial intelligence-driven analysis for broad host-pathogen interactions. Cell Microbiol 2021; 23:e13349. [PMID: 33930228 DOI: 10.1111/cmi.13349] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 04/21/2021] [Accepted: 04/26/2021] [Indexed: 12/15/2022]
Abstract
To study the dynamics of infection processes, it is common to manually enumerate imaging-based infection assays. However, manual counting of events from imaging data is biased, error-prone and a laborious task. We recently presented HRMAn (Host Response to Microbe Analysis), an automated image analysis program using state-of-the-art machine learning and artificial intelligence algorithms to analyse pathogen growth and host defence behaviour. With HRMAn, we can quantify intracellular infection by pathogens such as Toxoplasma gondii and Salmonella in a variety of cell types in an unbiased and highly reproducible manner, measuring multiple parameters including pathogen growth, pathogen killing and activation of host cell defences. Since HRMAn is based on the KNIME Analytics platform, it can easily be adapted to work with other pathogens and produce more readouts from quantitative imaging data. Here we showcase improvements to HRMAn resulting in the release of HRMAn 2.0 and new applications of HRMAn 2.0 for the analysis of host-pathogen interactions using the established pathogen T. gondii and further extend it for use with the bacterial pathogen Chlamydia trachomatis and the fungal pathogen Cryptococcus neoformans.
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Affiliation(s)
- Daniel Fisch
- Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, UK
- Host-Toxoplasma Interaction Laboratory, The Francis Crick Institute, London, UK
| | - Robert Evans
- Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, UK
- Host-Toxoplasma Interaction Laboratory, The Francis Crick Institute, London, UK
| | - Barbara Clough
- Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, UK
| | - Sophie K Byrne
- Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, UK
| | - Will M Channell
- Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, UK
| | - Jacob Dockterman
- Department of Immunology, Duke University Medical Center, Durham, North Carolina, USA
| | - Eva-Maria Frickel
- Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, UK
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27
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Fu M. Drug discovery from traditional Chinese herbal medicine using high content imaging technology. JOURNAL OF TRADITIONAL CHINESE MEDICAL SCIENCES 2021. [DOI: 10.1016/j.jtcms.2021.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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28
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Ren J, Han KY. 2.5D microscopy: Fast, high-throughput imaging via volumetric projection for quantitative subcellular analysis. ACS PHOTONICS 2021; 8:933-942. [PMID: 34485614 PMCID: PMC8412410 DOI: 10.1021/acsphotonics.1c00012] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Imaging-based single-cell analysis is essential to study the expression level and functions of biomolecules at subcellular resolution. However, its low throughput has prevented the measurement of numerous cellular features from multiples cells in a rapid and efficient manner. Here we report 2.5D microscopy that significantly improves the throughput of fluorescence imaging systems while maintaining high-resolution and single-molecule sensitivity. Instead of sequential z-scanning, volumetric information is projected onto a 2D image plane in a single shot by engineering the emitted fluorescence light. Our approach provides an improved imaging speed and uniform focal response within a specific imaging depth, which enabled us to perform quantitative single-molecule RNA measurements over a 2×2 mm2 region within an imaging depth of ~5 μm for mammalian cells in <10 min and immunofluorescence imaging at a >30 Hz volumetric frame rate with reduced photobleaching. Our microscope also offers the ability of multi-color imaging, depth control and super-resolution imaging.
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Affiliation(s)
- Jinhan Ren
- CREOL, The College of Optics and Photonics, University of Central Florida, Orlando, Florida 32816, United States
| | - Kyu Young Han
- CREOL, The College of Optics and Photonics, University of Central Florida, Orlando, Florida 32816, United States
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29
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PartSeg: a tool for quantitative feature extraction from 3D microscopy images for dummies. BMC Bioinformatics 2021; 22:72. [PMID: 33596823 PMCID: PMC7890960 DOI: 10.1186/s12859-021-03984-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 01/27/2021] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Bioimaging techniques offer a robust tool for studying molecular pathways and morphological phenotypes of cell populations subjected to various conditions. As modern high-resolution 3D microscopy provides access to an ever-increasing amount of high-quality images, there arises a need for their analysis in an automated, unbiased, and simple way. Segmentation of structures within the cell nucleus, which is the focus of this paper, presents a new layer of complexity in the form of dense packing and significant signal overlap. At the same time, the available segmentation tools provide a steep learning curve for new users with a limited technical background. This is especially apparent in the bulk processing of image sets, which requires the use of some form of programming notation. RESULTS In this paper, we present PartSeg, a tool for segmentation and reconstruction of 3D microscopy images, optimised for the study of the cell nucleus. PartSeg integrates refined versions of several state-of-the-art algorithms, including a new multi-scale approach for segmentation and quantitative analysis of 3D microscopy images. The features and user-friendly interface of PartSeg were carefully planned with biologists in mind, based on analysis of multiple use cases and difficulties encountered with other tools, to offer an ergonomic interface with a minimal entry barrier. Bulk processing in an ad-hoc manner is possible without the need for programmer support. As the size of datasets of interest grows, such bulk processing solutions become essential for proper statistical analysis of results. Advanced users can use PartSeg components as a library within Python data processing and visualisation pipelines, for example within Jupyter notebooks. The tool is extensible so that new functionality and algorithms can be added by the use of plugins. For biologists, the utility of PartSeg is presented in several scenarios, showing the quantitative analysis of nuclear structures. CONCLUSIONS In this paper, we have presented PartSeg which is a tool for precise and verifiable segmentation and reconstruction of 3D microscopy images. PartSeg is optimised for cell nucleus analysis and offers multi-scale segmentation algorithms best-suited for this task. PartSeg can also be used for the bulk processing of multiple images and its components can be reused in other systems or computational experiments.
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30
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Lago SG, Tomasik J, Bahn S. Functional patient-derived cellular models for neuropsychiatric drug discovery. Transl Psychiatry 2021; 11:128. [PMID: 33597511 PMCID: PMC7888004 DOI: 10.1038/s41398-021-01243-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 01/03/2021] [Accepted: 01/11/2021] [Indexed: 01/31/2023] Open
Abstract
Mental health disorders are a leading cause of disability worldwide. Challenges such as disease heterogeneity, incomplete characterization of the targets of existing drugs and a limited understanding of functional interactions of complex genetic risk loci and environmental factors have compromised the identification of novel drug candidates. There is a pressing clinical need for drugs with new mechanisms of action which address the lack of efficacy and debilitating side effects of current medications. Here we discuss a novel strategy for neuropsychiatric drug discovery which aims to address these limitations by identifying disease-related functional responses ('functional cellular endophenotypes') in a variety of patient-derived cells, such as induced pluripotent stem cell (iPSC)-derived neurons and organoids or peripheral blood mononuclear cells (PBMCs). Disease-specific alterations in cellular responses can subsequently yield novel drug screening targets and drug candidates. We discuss the potential of this approach in the context of recent advances in patient-derived cellular models, high-content single-cell screening of cellular networks and changes in the diagnostic framework of neuropsychiatric disorders.
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Affiliation(s)
- Santiago G. Lago
- grid.5335.00000000121885934Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
| | - Jakub Tomasik
- grid.5335.00000000121885934Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
| | - Sabine Bahn
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom.
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31
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Paran Y, Liron Y, Batsir S, Mabjeesh N, Geiger B, Kam Z. Multi-parametric characterization of drug effects on cells. F1000Res 2021; 9. [PMID: 33363713 PMCID: PMC7737707 DOI: 10.12688/f1000research.26254.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/13/2021] [Indexed: 12/28/2022] Open
Abstract
We present here a novel multi-parametric approach for the characterization of multiple cellular features, using images acquired by high-throughput and high-definition light microscopy. We specifically used this approach for deep and unbiased analysis of the effects of a drug library on five cultured cell lines. The presented method enables the acquisition and analysis of millions of images, of treated and control cells, followed by an automated identification of drugs inducing strong responses, evaluating the median effect concentrations and those cellular properties that are most highly affected by the drug. The tools described here provide standardized quantification of multiple attributes for systems level dissection of complex functions in normal and diseased cells, using multiple perturbations. Such analysis of cells, derived from pathological samples, may help in the diagnosis and follow-up of treatment in patients.
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Affiliation(s)
- Yael Paran
- Department of Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, 76100, Israel.,IDEA Biomedical Ltd., Rehovot, 76705, Israel
| | - Yuvalal Liron
- Department of Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Sarit Batsir
- Department of Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Nicola Mabjeesh
- Department of Urology, Tel Aviv Sourasky Medical Center, Tel Aviv, 64239, Israel
| | - Benjamin Geiger
- Department of Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, 76100, Israel.,Department of Immunology, The Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Zvi Kam
- Department of Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, 76100, Israel
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32
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Phillip JM, Han KS, Chen WC, Wirtz D, Wu PH. A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei. Nat Protoc 2021; 16:754-774. [PMID: 33424024 PMCID: PMC8167883 DOI: 10.1038/s41596-020-00432-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Accepted: 10/02/2020] [Indexed: 02/07/2023]
Abstract
Cell morphology encodes essential information on many underlying biological processes. It is commonly used by clinicians and researchers in the study, diagnosis, prognosis, and treatment of human diseases. Quantification of cell morphology has seen tremendous advances in recent years. However, effectively defining morphological shapes and evaluating the extent of morphological heterogeneity within cell populations remain challenging. Here we present a protocol and software for the analysis of cell and nuclear morphology from fluorescence or bright-field images using the VAMPIRE algorithm ( https://github.com/kukionfr/VAMPIRE_open ). This algorithm enables the profiling and classification of cells into shape modes based on equidistant points along cell and nuclear contours. Examining the distributions of cell morphologies across automatically identified shape modes provides an effective visualization scheme that relates cell shapes to cellular subtypes based on endogenous and exogenous cellular conditions. In addition, these shape mode distributions offer a direct and quantitative way to measure the extent of morphological heterogeneity within cell populations. This protocol is highly automated and fast, with the ability to quantify the morphologies from 2D projections of cells seeded both on 2D substrates or embedded within 3D microenvironments, such as hydrogels and tissues. The complete analysis pipeline can be completed within 60 minutes for a dataset of ~20,000 cells/2,400 images.
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Affiliation(s)
- Jude M Phillip
- Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences Oncology Center, Johns Hopkins Institute for Nanobiotechnology (INBT), Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Kyu-Sang Han
- Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences Oncology Center, Johns Hopkins Institute for Nanobiotechnology (INBT), Johns Hopkins University, Baltimore, MD, USA
| | - Wei-Chiang Chen
- Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences Oncology Center, Johns Hopkins Institute for Nanobiotechnology (INBT), Johns Hopkins University, Baltimore, MD, USA
| | - Denis Wirtz
- Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences Oncology Center, Johns Hopkins Institute for Nanobiotechnology (INBT), Johns Hopkins University, Baltimore, MD, USA.
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA.
| | - Pei-Hsun Wu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences Oncology Center, Johns Hopkins Institute for Nanobiotechnology (INBT), Johns Hopkins University, Baltimore, MD, USA.
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33
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Chandrasekaran SN, Ceulemans H, Boyd JD, Carpenter AE. Image-based profiling for drug discovery: due for a machine-learning upgrade? Nat Rev Drug Discov 2021; 20:145-159. [PMID: 33353986 PMCID: PMC7754181 DOI: 10.1038/s41573-020-00117-w] [Citation(s) in RCA: 133] [Impact Index Per Article: 44.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/13/2020] [Indexed: 12/20/2022]
Abstract
Image-based profiling is a maturing strategy by which the rich information present in biological images is reduced to a multidimensional profile, a collection of extracted image-based features. These profiles can be mined for relevant patterns, revealing unexpected biological activity that is useful for many steps in the drug discovery process. Such applications include identifying disease-associated screenable phenotypes, understanding disease mechanisms and predicting a drug's activity, toxicity or mechanism of action. Several of these applications have been recently validated and have moved into production mode within academia and the pharmaceutical industry. Some of these have yielded disappointing results in practice but are now of renewed interest due to improved machine-learning strategies that better leverage image-based information. Although challenges remain, novel computational technologies such as deep learning and single-cell methods that better capture the biological information in images hold promise for accelerating drug discovery.
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Affiliation(s)
| | - Hugo Ceulemans
- Discovery Data Sciences, Janssen Pharmaceutica NV, Beerse, Belgium
| | - Justin D Boyd
- High Content Imaging Technology Center, Internal Medicine Research Unit, Pfizer Inc., Cambridge, MA, USA
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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34
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Malandraki-Miller S, Riley PR. Use of artificial intelligence to enhance phenotypic drug discovery. Drug Discov Today 2021; 26:887-901. [PMID: 33484947 DOI: 10.1016/j.drudis.2021.01.013] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 12/28/2020] [Accepted: 01/15/2021] [Indexed: 01/17/2023]
Abstract
Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially taking into consideration reports of attrition rates and the colossal cost for drug development. The respective merits of the two main drug discovery approaches, phenotypic and target based, have divided opinion across the research community, because each hold different advantages for identifying novel molecular entities with a successful path to the market. Nevertheless, both have low translatability in the clinic. Artificial intelligence (AI) and adoption of machine learning (ML) tools offer the promise of revolutionising drug development, and overcoming obstacles in the drug discovery pipeline. Here, we assess the potential of target-driven and phenotypic-based approaches and offer a holistic description of the current state of the field, from both a scientific and industry perspective. With the emerging partnerships between AI/ML and pharma still in their relative infancy, we investigate the potential and current limitations with a particular focus on phenotypic drug discovery. Finally, we emphasise the value of public-private partnerships (PPPs) and cross-disciplinary collaborations to foster innovation and facilitate efficient drug discovery programmes.
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Affiliation(s)
| | - Paul R Riley
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.
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35
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Askjaer P, Harr JC. Genetic approaches to revealing the principles of nuclear architecture. Curr Opin Genet Dev 2020; 67:52-60. [PMID: 33338753 DOI: 10.1016/j.gde.2020.11.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 10/30/2020] [Accepted: 11/02/2020] [Indexed: 12/12/2022]
Abstract
The spatial organization of chromosomes inside the eukaryotic nucleus is important for DNA replication, repair and gene expression. During development of multicellular organisms, different compendiums of genes are either repressed or activated to produce specific cell types. Genetic manipulation of tractable organisms is invaluable to elucidate chromosome configuration and the underlying mechanisms. Systematic inhibition of genes through RNA interference and, more recently, CRISPR/Cas9-based screens have identified new proteins with significant roles in nuclear organization. Coupling this with advances in imaging techniques, such as multiplexed DNA fluorescence in situ hybridization, and with tissue-specific genome profiling by DNA adenine methylation identification has increased our knowledge about the immense complexity and dynamics of the nucleus.
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Affiliation(s)
- Peter Askjaer
- Andalusian Center for Developmental Biology (CABD), Consejo Superior de Investigaciones Científicas, Universidad Pablo de Olavide, Seville 41013, Spain.
| | - Jennifer C Harr
- Department of Biological Sciences, St. Mary's University, One Camino Santa Maria, San Antonio, TX, 78228, USA.
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36
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Zaki G, Gudla PR, Lee K, Kim J, Ozbun L, Shachar S, Gadkari M, Sun J, Fraser IDC, Franco LM, Misteli T, Pegoraro G. A Deep Learning Pipeline for Nucleus Segmentation. Cytometry A 2020; 97:1248-1264. [PMID: 33141508 DOI: 10.1002/cyto.a.24257] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 10/28/2020] [Accepted: 10/30/2020] [Indexed: 11/08/2022]
Abstract
Deep learning is rapidly becoming the technique of choice for automated segmentation of nuclei in biological image analysis workflows. In order to evaluate the feasibility of training nuclear segmentation models on small, custom annotated image datasets that have been augmented, we have designed a computational pipeline to systematically compare different nuclear segmentation model architectures and model training strategies. Using this approach, we demonstrate that transfer learning and tuning of training parameters, such as the composition, size, and preprocessing of the training image dataset, can lead to robust nuclear segmentation models, which match, and often exceed, the performance of existing, off-the-shelf deep learning models pretrained on large image datasets. We envision a practical scenario where deep learning nuclear segmentation models trained in this way can be shared across a laboratory, facility, or institution, and continuously improved by training them on progressively larger and varied image datasets. Our work provides computational tools and a practical framework for deep learning-based biological image segmentation using small annotated image datasets. Published [2020]. This article is a U.S. Government work and is in the public domain in the USA.
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Affiliation(s)
- George Zaki
- Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research (FNLCR), Frederick, Maryland, USA
| | - Prabhakar R Gudla
- High-Throughput Imaging Facility (HiTIF), Center for Cancer Research (CCR), NCI/NIH, Bethesda, Maryland, USA
| | - Kyunghun Lee
- High-Throughput Imaging Facility (HiTIF), Center for Cancer Research (CCR), NCI/NIH, Bethesda, Maryland, USA
| | - Justin Kim
- Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research (FNLCR), Frederick, Maryland, USA.,Pratt School of Engineering, Duke University, Durham, North Carolina, USA
| | - Laurent Ozbun
- High-Throughput Imaging Facility (HiTIF), Center for Cancer Research (CCR), NCI/NIH, Bethesda, Maryland, USA
| | - Sigal Shachar
- Cell Biology of Genomes (CBGE), Center for Cancer Research (CCR), NCI/NIH, Bethesda, Maryland, USA
| | - Manasi Gadkari
- Systemic Autoimmunity Branch, NIAMS/NIH, Bethesda, Maryland, USA
| | - Jing Sun
- Laboratory of Immune System Biology, NIAID/NIH, Bethesda, Maryland, USA
| | - Iain D C Fraser
- Laboratory of Immune System Biology, NIAID/NIH, Bethesda, Maryland, USA
| | - Luis M Franco
- Systemic Autoimmunity Branch, NIAMS/NIH, Bethesda, Maryland, USA
| | - Tom Misteli
- Cell Biology of Genomes (CBGE), Center for Cancer Research (CCR), NCI/NIH, Bethesda, Maryland, USA
| | - Gianluca Pegoraro
- High-Throughput Imaging Facility (HiTIF), Center for Cancer Research (CCR), NCI/NIH, Bethesda, Maryland, USA
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37
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Gruteser N, Kohlhas V, Balfanz S, Franzen A, Günther A, Offenhäusser A, Müller F, Nikolaev V, Lohse MJ, Baumann A. Establishing a sensitive fluorescence-based quantification method for cyclic nucleotides. BMC Biotechnol 2020; 20:47. [PMID: 32854679 PMCID: PMC7450941 DOI: 10.1186/s12896-020-00633-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 07/21/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Approximately 40% of prescribed drugs exert their activity via GTP-binding protein-coupled receptors (GPCRs). Once activated, these receptors cause transient changes in the concentration of second messengers, e.g., cyclic adenosine 3',5'-monophosphate (cAMP). Specific and efficacious genetically encoded biosensors have been developed to monitor cAMP fluctuations with high spatial and temporal resolution in living cells or tissue. A well characterized biosensor for cAMP is the Förster resonance energy transfer (FRET)-based Epac1-camps protein. Pharmacological characterization of newly developed ligands acting at GPCRs often includes numerical quantification of the second messenger amount that was produced. RESULTS To quantify cellular cAMP concentrations, we bacterially over-expressed and purified Epac1-camps and applied the purified protein in a cell-free detection assay for cAMP in a multi-well format. We found that the biosensor can detect as little as 0.15 pmol of cAMP, and that the sensitivity is not impaired by non-physiological salt concentrations or pH values. Notably, the assay tolerated desiccation and storage of the protein without affecting Epac1-camps cyclic nucleotide sensitivity. CONCLUSIONS We found that determination cAMP in lysates obtained from cell assays or tissue samples by purified Epac1-camps is a robust, fast, and sensitive assay suitable for routine and high throughput analyses.
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Affiliation(s)
- Nadine Gruteser
- Institute of Biological Information Processing (Molecular and Cellular Physiology, IBI-1), Forschungszentrum Jülich, 52428, Jülich, Germany
| | - Viktoria Kohlhas
- Institute of Biological Information Processing (Molecular and Cellular Physiology, IBI-1), Forschungszentrum Jülich, 52428, Jülich, Germany.,Present address: CECAD Research Center, 50931, Cologne, Germany
| | - Sabine Balfanz
- Institute of Biological Information Processing (Molecular and Cellular Physiology, IBI-1), Forschungszentrum Jülich, 52428, Jülich, Germany
| | - Arne Franzen
- Institute of Biological Information Processing (Molecular and Cellular Physiology, IBI-1), Forschungszentrum Jülich, 52428, Jülich, Germany
| | - Anne Günther
- Institute of Biological Information Processing (Molecular and Cellular Physiology, IBI-1), Forschungszentrum Jülich, 52428, Jülich, Germany.,Present address: RIKEN Center for Brain Science, Wako, Saitama, 351-0198, Japan
| | - Andreas Offenhäusser
- Institute of Biological Information Processing (Bioelectronics, IBI-3), Forschungszentrum Jülich, 52428, Jülich, Germany
| | - Frank Müller
- Institute of Biological Information Processing (Molecular and Cellular Physiology, IBI-1), Forschungszentrum Jülich, 52428, Jülich, Germany
| | - Viacheslav Nikolaev
- Institute of Experimental Cardiovascular Research, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Martin J Lohse
- Institute of Pharmacology and Toxicology, University of Würzburg, 97078, Würzburg, Germany.,Max Delbrück Center for Molecular Medicine, 13125, Berlin, Germany
| | - Arnd Baumann
- Institute of Biological Information Processing (Molecular and Cellular Physiology, IBI-1), Forschungszentrum Jülich, 52428, Jülich, Germany.
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Abstract
Infectious disease research spans scales from the molecular to the global—from specific mechanisms of pathogen drug resistance, virulence, and replication to the movement of people, animals, and pathogens around the world. All of these research areas have been impacted by the recent growth of large-scale data sources and data analytics. Some of these advances rely on data or analytic methods that are common to most biomedical data science, while others leverage the unique nature of infectious disease, namely its communicability. This review outlines major research progress in the past few years and highlights some remaining opportunities, focusing on data or methodological approaches particular to infectious disease.
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Affiliation(s)
- Peter M. Kasson
- Department of Biomedical Engineering and Department of Molecular Physiology, University of Virginia, Charlottesville, Virginia 22908, USA
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, 752 37 Uppsala, Sweden
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39
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de Anda-Jáuregui G, Hernández-Lemus E. Computational Oncology in the Multi-Omics Era: State of the Art. Front Oncol 2020; 10:423. [PMID: 32318338 PMCID: PMC7154096 DOI: 10.3389/fonc.2020.00423] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Accepted: 03/10/2020] [Indexed: 12/24/2022] Open
Abstract
Cancer is the quintessential complex disease. As technologies evolve faster each day, we are able to quantify the different layers of biological elements that contribute to the emergence and development of malignancies. In this multi-omics context, the use of integrative approaches is mandatory in order to gain further insights on oncological phenomena, and to move forward toward the precision medicine paradigm. In this review, we will focus on computational oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. We will discuss the current roles of computation in oncology in the context of multi-omic technologies, which include: data acquisition and processing; data management in the clinical and research settings; classification, diagnosis, and prognosis; and the development of models in the research setting, including their use for therapeutic target identification. We will discuss the machine learning and network approaches as two of the most promising emerging paradigms, in computational oncology. These approaches provide a foundation on how to integrate different layers of biological description into coherent frameworks that allow advances both in the basic and clinical settings.
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Affiliation(s)
- Guillermo de Anda-Jáuregui
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Cátedras Conacyt Para Jóvenes Investigadores, National Council on Science and Technology, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
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40
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Boyd J, Fennell M, Carpenter A. Harnessing the power of microscopy images to accelerate drug discovery: what are the possibilities? Expert Opin Drug Discov 2020; 15:639-642. [PMID: 32200648 DOI: 10.1080/17460441.2020.1743675] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Justin Boyd
- Internal Medicines Research Unit, Pfizer Inc ., Cambridge, MA, USA
| | - Myles Fennell
- Neuroscience and Platform Biology, Arvinas , New Haven, CT, USA
| | - Anne Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard , Cambridge, MA, USA
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41
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Li S, Xia M. Review of high-content screening applications in toxicology. Arch Toxicol 2019; 93:3387-3396. [PMID: 31664499 PMCID: PMC7011178 DOI: 10.1007/s00204-019-02593-5] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 10/08/2019] [Indexed: 12/17/2022]
Abstract
High-content screening (HCS) technology combining automated microscopy and quantitative image analysis can address biological questions in academia and the pharmaceutical industry. Various HCS experimental applications have been utilized in the research field of in vitro toxicology. In this review, we describe several HCS application approaches used for studying the mechanism of compound toxicity, highlight some challenges faced in the toxicological community, and discuss the future directions of HCS in regards to new models, new reagents, data management, and informatics. Many specialized areas of toxicology including developmental toxicity, genotoxicity, developmental neurotoxicity/neurotoxicity, hepatotoxicity, cardiotoxicity, and nephrotoxicity will be examined. In addition, several newly developed cellular assay models including induced pluripotent stem cells (iPSCs), three-dimensional (3D) cell models, and tissues-on-a-chip will be discussed. New genome-editing technologies (e.g., CRISPR/Cas9), data analyzing tools for imaging, and coupling with high-content assays will be reviewed. Finally, the applications of machine learning to image processing will be explored. These new HCS approaches offer a huge step forward in dissecting biological processes, developing drugs, and making toxicology studies easier.
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Affiliation(s)
- Shuaizhang Li
- Division for Pre-Clinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, 9800 Medical Center Drive, Bethesda, MD, USA
| | - Menghang Xia
- Division for Pre-Clinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, 9800 Medical Center Drive, Bethesda, MD, USA.
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42
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Schau GF, Thibault G, Dane MA, Gray JW, Heiser LM, Chang YH. Variational Autoencoding Tissue Response to Microenvironment Perturbation. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2019; 10949. [PMID: 31379401 DOI: 10.1117/12.2512660] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
This work applies deep variational autoencoder learning architecture to study multi-cellular growth characteristics of human mammary epithelial cells in response to diverse microenvironment perturbations. Our approach introduces a novel method of visualizing learned feature spaces of trained variational autoencoding models that enables visualization of principal features in two dimensions. We find that unsupervised learned features more closely associate with expert annotation of cell colony organization than biologically-inspired hand-crafted features, demonstrating the utility of deep learning systems to meaningfully characterize features of multi-cellular growth characteristics in a fully unsupervised and data-driven manner.
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Affiliation(s)
- Geoffrey F Schau
- Dept. of Biomedical Engineering, Center for Spatial Systems Biomedicine, Oregon Health & Science University, Portland, OR, USA.,Computational Biology Program, Oregon Health & Science University, Portland, OR, USA
| | - Guillaume Thibault
- Dept. of Biomedical Engineering, Center for Spatial Systems Biomedicine, Oregon Health & Science University, Portland, OR, USA
| | - Mark A Dane
- Dept. of Biomedical Engineering, Center for Spatial Systems Biomedicine, Oregon Health & Science University, Portland, OR, USA
| | - Joe W Gray
- Dept. of Biomedical Engineering, Center for Spatial Systems Biomedicine, Oregon Health & Science University, Portland, OR, USA
| | - Laura M Heiser
- Dept. of Biomedical Engineering, Center for Spatial Systems Biomedicine, Oregon Health & Science University, Portland, OR, USA
| | - Young Hwan Chang
- Dept. of Biomedical Engineering, Center for Spatial Systems Biomedicine, Oregon Health & Science University, Portland, OR, USA.,Computational Biology Program, Oregon Health & Science University, Portland, OR, USA
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43
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Bai Y, Zhang D, Lan L, Huang Y, Maize K, Shakouri A, Cheng JX. Ultrafast chemical imaging by widefield photothermal sensing of infrared absorption. SCIENCE ADVANCES 2019; 5:eaav7127. [PMID: 31334347 PMCID: PMC6641941 DOI: 10.1126/sciadv.aav7127] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 06/14/2019] [Indexed: 05/19/2023]
Abstract
Infrared (IR) imaging has become a viable tool for visualizing various chemical bonds in a specimen. The performance, however, is limited in terms of spatial resolution and imaging speed. Here, instead of measuring the loss of the IR beam, we use a pulsed visible light for high-throughput, widefield sensing of the transient photothermal effect induced by absorption of single mid-IR pulses. To extract these transient signals, we built a virtual lock-in camera synchronized to the visible probe and IR light pulses with precisely controlled delays, allowing submicrosecond temporal resolution determined by the probe pulse width. Our widefield photothermal sensing microscope enabled chemical imaging at a speed up to 1250 frames/s, with high spectral fidelity, while offering submicrometer spatial resolution. With the capability of imaging living cells and nanometer-scale polymer films, widefield photothermal microscopy opens a new way for high-throughput characterization of biological and material specimens.
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Affiliation(s)
- Yeran Bai
- Key Laboratory of High Power Laser and Physics, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
- Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA
- Photonics Center, Boston University, Boston, MA 02215, USA
| | - Delong Zhang
- Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA
- Photonics Center, Boston University, Boston, MA 02215, USA
| | - Lu Lan
- Photonics Center, Boston University, Boston, MA 02215, USA
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Yimin Huang
- Photonics Center, Boston University, Boston, MA 02215, USA
- Department of Chemistry, Boston University, Boston, MA 02215, USA
| | - Kerry Maize
- Birck Nanotechnology Center, Purdue University, West Lafayette, IN 47906, USA
| | - Ali Shakouri
- Birck Nanotechnology Center, Purdue University, West Lafayette, IN 47906, USA
- Corresponding author. (J.-X.C.); (A.S.)
| | - Ji-Xin Cheng
- Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA
- Photonics Center, Boston University, Boston, MA 02215, USA
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Department of Chemistry, Boston University, Boston, MA 02215, USA
- Corresponding author. (J.-X.C.); (A.S.)
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44
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Friese A, Ursu A, Hochheimer A, Schöler HR, Waldmann H, Bruder JM. The Convergence of Stem Cell Technologies and Phenotypic Drug Discovery. Cell Chem Biol 2019; 26:1050-1066. [PMID: 31231030 DOI: 10.1016/j.chembiol.2019.05.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Revised: 04/04/2019] [Accepted: 05/20/2019] [Indexed: 02/06/2023]
Abstract
Recent advances in induced pluripotent stem cell technologies and phenotypic screening shape the future of bioactive small-molecule discovery. In this review we analyze the impact of small-molecule phenotypic screens on drug discovery as well as on the investigation of human development and disease biology. We further examine the role of 3D spheroid/organoid structures, microfluidic systems, and miniaturized on-a-chip systems for future discovery strategies. In highlighting representative examples, we analyze how recent achievements can translate into future therapies. Finally, we discuss remaining challenges that need to be overcome for the adaptation of the next generation of screening approaches.
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Affiliation(s)
- Alexandra Friese
- Department of Chemical Biology, Max Planck Institute of Molecular Physiology, 44227 Dortmund, Germany
| | - Andrei Ursu
- Department of Chemical Biology, Max Planck Institute of Molecular Physiology, 44227 Dortmund, Germany; Department of Chemistry, The Scripps Research Institute, Jupiter, FL 33458, USA; Faculty of Chemistry and Chemical Biology, TU Dortmund, Otto-Hahn-Str. 4a, 44227 Dortmund, Germany
| | - Andreas Hochheimer
- ISAR Bioscience GmbH, Institute for Stem Cell & Applied Regenerative Medicine Research, 82152 Planegg, Germany
| | - Hans R Schöler
- Department of Cell and Developmental Biology, Max Planck Institute for Molecular Biomedicine, 48149 Münster, Germany; Medical Faculty, University of Münster, Domagkstrasse 3, 48149 Münster, Germany.
| | - Herbert Waldmann
- Department of Chemical Biology, Max Planck Institute of Molecular Physiology, 44227 Dortmund, Germany; Faculty of Chemistry and Chemical Biology, TU Dortmund, Otto-Hahn-Str. 4a, 44227 Dortmund, Germany.
| | - Jan M Bruder
- Department of Cell and Developmental Biology, Max Planck Institute for Molecular Biomedicine, 48149 Münster, Germany.
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45
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Zhang Y, Xie Y, Liu W, Deng W, Peng D, Wang C, Xu H, Ruan C, Deng Y, Guo Y, Lu C, Yi C, Ren J, Xue Y. DeepPhagy: a deep learning framework for quantitatively measuring autophagy activity in Saccharomyces cerevisiae. Autophagy 2019; 16:626-640. [PMID: 31204567 DOI: 10.1080/15548627.2019.1632622] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Seeing is believing. The direct observation of GFP-Atg8 vacuolar delivery under confocal microscopy is one of the most useful end-point measurements for monitoring yeast macroautophagy/autophagy. However, manually labelling individual cells from large-scale sets of images is time-consuming and labor-intensive, which has greatly hampered its extensive use in functional screens. Herein, we conducted a time-course analysis of nitrogen starvation-induced autophagy in wild-type and knockout mutants of 35 AuTophaGy-related (ATG) genes in Saccharomyces cerevisiae and obtained 1,944 confocal images containing > 200,000 cells. We manually labelled 8,078 autophagic and 18,493 non-autophagic cells as a benchmark dataset and developed a new deep learning tool for autophagy (DeepPhagy), which exhibited superior accuracy in recognizing autophagic cells compared to other existing methods, with an area under the curve (AUC) value of 0.9710 from 10-fold cross-validations. We further used DeepPhagy to automatically analyze all the images and quantitatively classified the autophagic phenotypes of the 35 atg knockout mutants into 3 classes. The high consistency in our computational and biochemical results indicated the reliability of DeepPhagy for measuring autophagic activity. Moreover, we used DeepPhagy to analyze 3 additional types of autophagic phenotypes, including the targeting of Atg1-GFP to the vacuole, the vacuolar delivery of GFP-Atg19, and the disintegration of autophagic bodies indicated by GFP-Atg8, all with satisfying accuracies. Taken together, our study not only enables the GFP-Atg8 fluorescence assay to become a quantitative measurement for analyzing autophagic phenotypes in S. cerevisiae but also demonstrates that deep learning-based methods could potentially be applied to different types of autophagy.Abbreviations: Ac: accuracy; ALP: alkaline phosphatase; ALR: autophagic lysosomal reformation; ATG: AuTophaGy-related; AUC: area under the curve; CNN: convolutional neural network; Cvt: cytoplasm-to-vacuole targeting; DeepPhagy: deep learning for autophagy; fc_2: second fully connected; GFP: green fluorescent protein; MAP1LC3/LC3: microtubule-associated protein 1 light chain 3 beta; HAT: histone acetyltransferase; HemI: Heat map Illustrator; JRE: Java Runtime Environment; KO: knockout; LRN: local response normalization; MCC: Mathew Correlation Coefficient; OS: operating system; PAS: phagophore assembly site; PC: principal component; PCA: principal component analysis; PPI: protein-protein interaction; Pr: precision; QPSO: Quantum-behaved Particle Swarm Optimization; ReLU: rectified linear unit; RF: random forest; ROC: receiver operating characteristic; ROI: region of interest; SD: systematic derivation; SGD: stochastic gradient descent; Sn: sensitivity; Sp: specificity; SRG: seeded region growing; t-SNE: t-distributed stochastic neighbor embedding; 2D: 2-dimensional; WT: wild-type.
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Affiliation(s)
- Ying Zhang
- Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Yubin Xie
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Wenzhong Liu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Wankun Deng
- Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Di Peng
- Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Chenwei Wang
- Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Haodong Xu
- Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Chen Ruan
- Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Yongjie Deng
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yaping Guo
- Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Chenjun Lu
- Department of Biochemistry and Molecular Biology, Program in Molecular and Cell Biology, Zhejiang University School of Medicine, Hangzhou, China
| | - Cong Yi
- Department of Biochemistry and Molecular Biology, Program in Molecular and Cell Biology, Zhejiang University School of Medicine, Hangzhou, China
| | - Jian Ren
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yu Xue
- Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
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46
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Lee KCM, Wang M, Cheah KSE, Chan GCF, So HKH, Wong KKY, Tsia KK. Quantitative Phase Imaging Flow Cytometry for Ultra-Large-Scale Single-Cell Biophysical Phenotyping. Cytometry A 2019; 95:510-520. [PMID: 31012276 DOI: 10.1002/cyto.a.23765] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 03/19/2019] [Accepted: 04/01/2019] [Indexed: 12/21/2022]
Abstract
Cellular biophysical properties are the effective label-free phenotypes indicative of differences in cell types, states, and functions. However, current biophysical phenotyping methods largely lack the throughput and specificity required in the majority of cell-based assays that involve large-scale single-cell characterization for inquiring the inherently complex heterogeneity in many biological systems. Further confounded by the lack of reported robust reproducibility and quality control, widespread adoption of single-cell biophysical phenotyping in mainstream cytometry remains elusive. To address this challenge, here we present a label-free imaging flow cytometer built upon a recently developed ultrafast quantitative phase imaging (QPI) technique, coined multi-ATOM, that enables label-free single-cell QPI, from which a multitude of subcellularly resolvable biophysical phenotypes can be parametrized, at an experimentally recorded throughput of >10,000 cells/s-a capability that is otherwise inaccessible in current QPI. With the aim to translate multi-ATOM into mainstream cytometry, we report robust system calibration and validation (from image acquisition to phenotyping reproducibility) and thus demonstrate its ability to establish high-dimensional single-cell biophysical phenotypic profiles at ultra-large-scale (>1,000,000 cells). Such a combination of throughput and content offers sufficiently high label-free statistical power to classify multiple human leukemic cell types at high accuracy (~92-97%). This system could substantiate the significance of high-throughput QPI flow cytometry in enabling next frontier in large-scale image-derived single-cell analysis applied in biological discovery and cost-effective clinical diagnostics. © 2019 International Society for Advancement of Cytometry.
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Affiliation(s)
- Kelvin C M Lee
- Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Hong Kong, Pokfulam, Hong Kong
| | - Maolin Wang
- Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Hong Kong, Pokfulam, Hong Kong
| | - Kathryn S E Cheah
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Godfrey C F Chan
- Department of Pediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Hayden K H So
- Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Hong Kong, Pokfulam, Hong Kong
| | - Kenneth K Y Wong
- Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Hong Kong, Pokfulam, Hong Kong
| | - Kevin K Tsia
- Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Hong Kong, Pokfulam, Hong Kong
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47
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Jiao Y, Ahmed U, Sim MFM, Bejar A, Zhang X, Talukder MMU, Rice R, Flannick J, Podgornaia AI, Reilly DF, Engreitz JM, Kost-Alimova M, Hartland K, Mercader JM, Georges S, Wagh V, Tadin-Strapps M, Doench JG, Edwardson JM, Rochford JJ, Rosen ED, Majithia AR. Discovering metabolic disease gene interactions by correlated effects on cellular morphology. Mol Metab 2019; 24:108-119. [PMID: 30940487 PMCID: PMC6531784 DOI: 10.1016/j.molmet.2019.03.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 03/06/2019] [Accepted: 03/07/2019] [Indexed: 12/26/2022] Open
Abstract
Objective Impaired expansion of peripheral fat contributes to the pathogenesis of insulin resistance and Type 2 Diabetes (T2D). We aimed to identify novel disease–gene interactions during adipocyte differentiation. Methods Genes in disease-associated loci for T2D, adiposity and insulin resistance were ranked according to expression in human adipocytes. The top 125 genes were ablated in human pre-adipocytes via CRISPR/CAS9 and the resulting cellular phenotypes quantified during adipocyte differentiation with high-content microscopy and automated image analysis. Morphometric measurements were extracted from all images and used to construct morphologic profiles for each gene. Results Over 107 morphometric measurements were obtained. Clustering of the morphologic profiles accross all genes revealed a group of 14 genes characterized by decreased lipid accumulation, and enriched for known lipodystrophy genes. For two lipodystrophy genes, BSCL2 and AGPAT2, sub-clusters with PLIN1 and CEBPA identifed by morphological similarity were validated by independent experiments as novel protein–protein and gene regulatory interactions. Conclusions A morphometric approach in adipocytes can resolve multiple cellular mechanisms for metabolic disease loci; this approach enables mechanistic interrogation of the hundreds of metabolic disease loci whose function still remains unknown. Loss-of-function genetic screen in human adipocytes for 125 genes selected from metabolic disease-associated loci. Genetic screen read out by cellular morphometry— 77,000 images taken with 400 morphological features extracted per image. Pairwise mechanistic interactions between genes identified by correlations of cellular morphometry—two interactions validated. Novel interaction between BSCL2 and PLIN1 from biophysical association of proteins at lipid droplet surface. Novel interaction between CEBPA and AGPAT2 from CEBPA dependent transcription of AGPAT2.
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Affiliation(s)
- Yang Jiao
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Umer Ahmed
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - M F Michelle Sim
- University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
| | - Andrea Bejar
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Xiaolan Zhang
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Robert Rice
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jason Flannick
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Anna I Podgornaia
- Genetics and Pharmacogenomics, Merck & Co., Inc., Boston, MA 02115, USA
| | - Dermot F Reilly
- Genetics and Pharmacogenomics, Merck & Co., Inc., Boston, MA 02115, USA
| | | | | | - Kate Hartland
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Sara Georges
- Genetics and Pharmacogenomics, Merck & Co., Inc., Boston, MA 02115, USA
| | - Vilas Wagh
- Genetics and Pharmacogenomics, Merck & Co., Inc., Boston, MA 02115, USA
| | | | - John G Doench
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Justin J Rochford
- University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK; Rowett Institute and the Aberdeen Cardiovascular and Diabetes Centre, University of Aberdeen, Foresterhill, Aberdeen AB25 2ZD, UK
| | - Evan D Rosen
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Division of Endocrinology, Diabetes and Obesity, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA; Harvard Medical School, Department of Genetics, Boston, MA 02215, USA
| | - Amit R Majithia
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Division of Endocrinology, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA.
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48
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Iannetti EF, Prigione A, Smeitink JAM, Koopman WJH, Beyrath J, Renkema H. Live-Imaging Readouts and Cell Models for Phenotypic Profiling of Mitochondrial Function. Front Genet 2019; 10:131. [PMID: 30881379 PMCID: PMC6405630 DOI: 10.3389/fgene.2019.00131] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 02/06/2019] [Indexed: 12/16/2022] Open
Abstract
Mitochondria are best known as the powerhouses of the cells but their cellular role goes far beyond energy production; among others, they have a pivotal function in cellular calcium and redox homeostasis. Mitochondrial dysfunction is often associated with severe and relatively rare disorders with an unmet therapeutic need. Given their central integrating role in multiple cellular pathways, mitochondrial dysfunction is also relevant in the pathogenesis of various other, more common, human pathologies. Here we discuss how live-cell high content microscopy can be used for image-based phenotypic profiling to assess mitochondrial (dys) function. From this perspective, we discuss a selection of live-cell fluorescent reporters and imaging strategies and discuss the pros/cons of human cell models in mitochondrial research. We also present an overview of live-cell high content microscopy applications used to detect disease-associated cellular phenotypes and perform cell-based drug screening.
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Affiliation(s)
- Eligio F. Iannetti
- Khondrion BV, Nijmegen, Netherlands
- Department of Biochemistry, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | | | - Jan A. M. Smeitink
- Khondrion BV, Nijmegen, Netherlands
- Radboud Center for Mitochondrial Medicine, Radboud University Medical Center, Nijmegen, Netherlands
| | - Werner J. H. Koopman
- Department of Biochemistry, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
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49
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Lago SG, Bahn S. Clinical Trials and Therapeutic Rationale for Drug Repurposing in Schizophrenia. ACS Chem Neurosci 2019; 10:58-78. [PMID: 29944339 DOI: 10.1021/acschemneuro.8b00205] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
There is a paucity of efficacious novel drugs to address high rates of treatment resistance and refractory symptoms in schizophrenia. The identification of novel therapeutic indications for approved drugs-drug repurposing-has the potential to expedite clinical trials and reduce the costly risk of failure which currently limits central nervous system drug discovery efforts. In the present Review we discuss the historical role of drug repurposing in schizophrenia drug discovery and review the main classes of repurposing candidates currently in clinical trials for schizophrenia in terms of their therapeutic rationale, mechanisms of action, and preliminary results from clinical trials. Subsequently we outline the challenges and limitations which face the clinical repurposing pipeline and how novel technologies might serve to address these.
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Affiliation(s)
- Santiago G. Lago
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, U.K
| | - Sabine Bahn
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, U.K
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50
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Rodrigues Lopes I, Silva RJ, Caramelo I, Eulalio A, Mano M. Shedding light on microRNA function via microscopy-based screening. Methods 2019; 152:55-64. [DOI: 10.1016/j.ymeth.2018.09.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2018] [Revised: 09/13/2018] [Accepted: 09/28/2018] [Indexed: 12/24/2022] Open
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