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Huang J, Luo Y, Guo Y, Li W, Wang Z, Liu G, Yang G. Accurate segmentation of intracellular organelle networks using low-level features and topological self-similarity. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae559. [PMID: 39302662 DOI: 10.1093/bioinformatics/btae559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 08/12/2024] [Accepted: 09/19/2024] [Indexed: 09/22/2024]
Abstract
MOTIVATION Intracellular organelle networks (IONs) such as the endoplasmic reticulum (ER) network and the mitochondrial (MITO) network serve crucial physiological functions. The morphology of these networks plays a critical role in mediating their functions. Accurate image segmentation is required for analyzing the morphology and topology of these networks for applications such as molecular mechanism analysis and drug target screening. So far, however, progress has been hindered by their structural complexity and density. RESULTS In this study, we first establish a rigorous performance baseline for accurate segmentation of these organelle networks from fluorescence microscopy images by optimizing a baseline U-Net model. We then develop the multi-resolution encoder (MRE) and the hierarchical fusion loss (Lhf) based on two inductive components, namely low-level features and topological self-similarity, to assist the model in better adapting to the task of segmenting IONs. Empowered by MRE and Lhf, both U-Net and Pyramid Vision Transformer (PVT) outperform competing state-of-the-art models such as U-Net++, HR-Net, nnU-Net, and TransUNet on custom datasets of the ER network and the MITO network, as well as on public datasets of another biological network, the retinal blood vessel network. In addition, integrating MRE and Lhf with models such as HR-Net and TransUNet also enhances their segmentation performance. These experimental results confirm the generalization capability and potential of our approach. Furthermore, accurate segmentation of the ER network enables analysis that provides novel insights into its dynamic morphological and topological properties. AVAILABILITY AND IMPLEMENTATION Code and data are openly accessible at https://github.com/cbmi-group/MRE.
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Affiliation(s)
- Jiaxing Huang
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yaoru Luo
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuanhao Guo
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wenjing Li
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zichen Wang
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Guole Liu
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ge Yang
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
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2
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Rood JE, Hupalowska A, Regev A. Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas. Cell 2024; 187:4520-4545. [PMID: 39178831 DOI: 10.1016/j.cell.2024.07.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Revised: 07/15/2024] [Accepted: 07/21/2024] [Indexed: 08/26/2024]
Abstract
Comprehensively charting the biologically causal circuits that govern the phenotypic space of human cells has often been viewed as an insurmountable challenge. However, in the last decade, a suite of interleaved experimental and computational technologies has arisen that is making this fundamental goal increasingly tractable. Pooled CRISPR-based perturbation screens with high-content molecular and/or image-based readouts are now enabling researchers to probe, map, and decipher genetically causal circuits at increasing scale. This scale is now eminently suitable for the deployment of artificial intelligence and machine learning (AI/ML) to both direct further experiments and to predict or generate information that was not-and sometimes cannot-be gathered experimentally. By combining and iterating those through experiments that are designed for inference, we now envision a Perturbation Cell Atlas as a generative causal foundation model to unify human cell biology.
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Affiliation(s)
| | | | - Aviv Regev
- Genentech, South San Francisco, CA, USA.
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3
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Keikhosravi A, Almansour F, Bohrer CH, Fursova NA, Guin K, Sood V, Misteli T, Larson DR, Pegoraro G. High-throughput image processing software for the study of nuclear architecture and gene expression. Sci Rep 2024; 14:18426. [PMID: 39117696 PMCID: PMC11310328 DOI: 10.1038/s41598-024-66600-1] [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: 10/27/2023] [Accepted: 07/02/2024] [Indexed: 08/10/2024] Open
Abstract
High-throughput imaging (HTI) generates complex imaging datasets from a large number of experimental perturbations. Commercial HTI software programs for image analysis workflows typically do not allow full customization and adoption of new image processing algorithms in the analysis modules. While open-source HTI analysis platforms provide individual modules in the workflow, like nuclei segmentation, spot detection, or cell tracking, they are often limited in integrating novel analysis modules or algorithms. Here, we introduce the High-Throughput Image Processing Software (HiTIPS) to expand the range and customization of existing HTI analysis capabilities. HiTIPS incorporates advanced image processing and machine learning algorithms for automated cell and nuclei segmentation, spot signal detection, nucleus tracking, nucleus registration, spot tracking, and quantification of spot signal intensity. Furthermore, HiTIPS features a graphical user interface that is open to integration of new analysis modules for existing analysis pipelines and to adding new analysis modules. To demonstrate the utility of HiTIPS, we present three examples of image analysis workflows for high-throughput DNA FISH, immunofluorescence (IF), and live-cell imaging of transcription in single cells. Altogether, we demonstrate that HiTIPS is a user-friendly, flexible, and open-source HTI software platform for a variety of cell biology applications.
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Affiliation(s)
- Adib Keikhosravi
- High-Throughput Imaging Facility, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Faisal Almansour
- Cell Biology of Genomes, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
- Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical School, Washington, DC, 20057, USA
| | - Christopher H Bohrer
- System Biology of Gene Expression, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Nadezda A Fursova
- System Biology of Gene Expression, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Krishnendu Guin
- Cell Biology of Genomes, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Varun Sood
- Cell Biology of Genomes, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
- System Biology of Gene Expression, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Tom Misteli
- Cell Biology of Genomes, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Daniel R Larson
- System Biology of Gene Expression, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Gianluca Pegoraro
- High-Throughput Imaging Facility, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA.
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4
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Yu M, Li W, Yu Y, Zhao Y, Xiao L, Lauschke VM, Cheng Y, Zhang X, Wang Y. Deep learning large-scale drug discovery and repurposing. NATURE COMPUTATIONAL SCIENCE 2024; 4:600-614. [PMID: 39169261 DOI: 10.1038/s43588-024-00679-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 07/17/2024] [Indexed: 08/23/2024]
Abstract
Large-scale drug discovery and repurposing is challenging. Identifying the mechanism of action (MOA) is crucial, yet current approaches are costly and low-throughput. Here we present an approach for MOA identification by profiling changes in mitochondrial phenotypes. By temporally imaging mitochondrial morphology and membrane potential, we established a pipeline for monitoring time-resolved mitochondrial images, resulting in a dataset comprising 570,096 single-cell images of cells exposed to 1,068 United States Food and Drug Administration-approved drugs. A deep learning model named MitoReID, using a re-identification (ReID) framework and an Inflated 3D ResNet backbone, was developed. It achieved 76.32% Rank-1 and 65.92% mean average precision on the testing set and successfully identified the MOAs for six untrained drugs on the basis of mitochondrial phenotype. Furthermore, MitoReID identified cyclooxygenase-2 inhibition as the MOA of the natural compound epicatechin in tea, which was successfully validated in vitro. Our approach thus provides an automated and cost-effective alternative for target identification that could accelerate large-scale drug discovery and repurposing.
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Affiliation(s)
- Min Yu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | | | - Yunru Yu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yu Zhao
- School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Lizhi Xiao
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institute, Stockholm, Sweden
| | - Yiyu Cheng
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
- State Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, China.
| | - Xingcai Zhang
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA.
| | - Yi Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
- State Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, China.
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Hangzhou, China.
- Center for system biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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5
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Liu J, Gao F, Zhang L, Yang H. A Saturation Artifacts Inpainting Method Based on Two-Stage GAN for Fluorescence Microscope Images. MICROMACHINES 2024; 15:928. [PMID: 39064439 PMCID: PMC11279111 DOI: 10.3390/mi15070928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2024] [Revised: 07/10/2024] [Accepted: 07/18/2024] [Indexed: 07/28/2024]
Abstract
Fluorescence microscopic images of cells contain a large number of morphological features that are used as an unbiased source of quantitative information about cell status, through which researchers can extract quantitative information about cells and study the biological phenomena of cells through statistical and analytical analysis. As an important research object of phenotypic analysis, images have a great influence on the research results. Saturation artifacts present in the image result in a loss of grayscale information that does not reveal the true value of fluorescence intensity. From the perspective of data post-processing, we propose a two-stage cell image recovery model based on a generative adversarial network to solve the problem of phenotypic feature loss caused by saturation artifacts. The model is capable of restoring large areas of missing phenotypic features. In the experiment, we adopt the strategy of progressive restoration to improve the robustness of the training effect and add the contextual attention structure to enhance the stability of the restoration effect. We hope to use deep learning methods to mitigate the effects of saturation artifacts to reveal how chemical, genetic, and environmental factors affect cell state, providing an effective tool for studying the field of biological variability and improving image quality in analysis.
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Affiliation(s)
- Jihong Liu
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (F.G.); (L.Z.)
| | - Fei Gao
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (F.G.); (L.Z.)
| | - Lvheng Zhang
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (F.G.); (L.Z.)
| | - Haixu Yang
- Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China;
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6
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Xu Y, Han Y, Liu L, Han S, Zou S, Cheng B, Wang F, Xie X, Liang Y, Song M, Pang S. Highly sensitive response to the toxicity of environmental chemicals in transparent casper zebrafish. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 948:174865. [PMID: 39032757 DOI: 10.1016/j.scitotenv.2024.174865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 06/15/2024] [Accepted: 07/16/2024] [Indexed: 07/23/2024]
Abstract
The response sensitivity to toxic substances is the most concerned performance of animal model in chemical risk assessment. Casper (mitfaw2/w2;mpv17a9/a9), a transparent zebrafish mutant, is a useful in vivo model for toxicological assessment. However, the ability of casper to respond to the toxicity of exogenous chemicals is unknown. In this study, zebrafish embryos were exposed to five environmental chemicals, chlorpyrifos, lindane, α-endosulfan, bisphenol A, tetrabromobisphenol A (TBBPA), and an antiepileptic drug valproic acid. The half-lethal concentration (LC50) values of these chemicals in casper embryos were 62-87 % of that in the wild-type. After TBBPA exposure, the occurrence of developmental defects in the posterior blood island of casper embryos was increased by 67-77 % in relative to the wild-type, and the half-maximal effective concentration (EC50) in casper was 73 % of that in the wild-type. Moreover, the casper genetic background significantly increased the hyperlocomotion caused by chlorpyrifos and lindane exposure compared with the wild-type. These results demonstrated that casper had greater susceptibility to toxicity than wild-type zebrafish in acute toxicity, developmental toxicity and neurobehavioral toxicity assessments. Our data will inform future toxicological studies in casper and accelerate the development of efficient approaches and strategies for toxicity assessment via the use of casper.
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Affiliation(s)
- Yingjun Xu
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China; School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan 430205, China
| | - Yiming Han
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Li Liu
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China; School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan 430205, China
| | - Shanshan Han
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Shibiao Zou
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Bo Cheng
- School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan 430205, China
| | - Fengbang Wang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Xunwei Xie
- China Zebrafish Resource Center, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
| | - Yong Liang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Maoyong Song
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China; State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
| | - Shaochen Pang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China; School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan 430205, China.
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7
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Saunders N, Monel B, Cayet N, Archetti L, Moreno H, Jeanne A, Marguier A, Buchrieser J, Wai T, Schwartz O, Fréchin M. Dynamic label-free analysis of SARS-CoV-2 infection reveals virus-induced subcellular remodeling. Nat Commun 2024; 15:4996. [PMID: 38862527 PMCID: PMC11166935 DOI: 10.1038/s41467-024-49260-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 05/30/2024] [Indexed: 06/13/2024] Open
Abstract
Assessing the impact of SARS-CoV-2 on organelle dynamics allows a better understanding of the mechanisms of viral replication. We combine label-free holotomographic microscopy with Artificial Intelligence to visualize and quantify the subcellular changes triggered by SARS-CoV-2 infection. We study the dynamics of shape, position and dry mass of nucleoli, nuclei, lipid droplets and mitochondria within hundreds of single cells from early infection to syncytia formation and death. SARS-CoV-2 infection enlarges nucleoli, perturbs lipid droplets, changes mitochondrial shape and dry mass, and separates lipid droplets from mitochondria. We then used Bayesian network modeling on organelle dry mass states to define organelle cross-regulation networks and report modifications of organelle cross-regulation that are triggered by infection and syncytia formation. Our work highlights the subcellular remodeling induced by SARS-CoV-2 infection and provides an Artificial Intelligence-enhanced, label-free methodology to study in real-time the dynamics of cell populations and their content.
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Affiliation(s)
- Nell Saunders
- Virus & Immunity Unit, Institut Pasteur, Université Paris Cité, CNRS, UMR 3569, Paris, France
| | - Blandine Monel
- Virus & Immunity Unit, Institut Pasteur, Université Paris Cité, CNRS, UMR 3569, Paris, France
| | - Nadège Cayet
- Institut Pasteur, Université Paris Cité, Ultrastructural Bioimaging Unit, 75015, Paris, France
| | - Lorenzo Archetti
- Deep Quantitative Biology Department, Nanolive SA, Tolochenaz, Switzerland
| | - Hugo Moreno
- Deep Quantitative Biology Department, Nanolive SA, Tolochenaz, Switzerland
| | - Alexandre Jeanne
- Deep Quantitative Biology Department, Nanolive SA, Tolochenaz, Switzerland
| | - Agathe Marguier
- Deep Quantitative Biology Department, Nanolive SA, Tolochenaz, Switzerland
| | - Julian Buchrieser
- Virus & Immunity Unit, Institut Pasteur, Université Paris Cité, CNRS, UMR 3569, Paris, France
| | - Timothy Wai
- Mitochondrial Biology Group, Institut Pasteur, Université Paris Cité, CNRS, UMR 3691, Paris, France
| | - Olivier Schwartz
- Virus & Immunity Unit, Institut Pasteur, Université Paris Cité, CNRS, UMR 3569, Paris, France.
- Vaccine Research Institute, Creteil, France.
| | - Mathieu Fréchin
- Deep Quantitative Biology Department, Nanolive SA, Tolochenaz, Switzerland.
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8
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Marzi A, Eder KM, Barroso Á, Kemper B, Schnekenburger J. Quantitative Phase Imaging as Sensitive Screening Method for Nanoparticle-Induced Cytotoxicity Assessment. Cells 2024; 13:697. [PMID: 38667312 PMCID: PMC11049110 DOI: 10.3390/cells13080697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/10/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
The assessment of nanoparticle cytotoxicity is challenging due to the lack of customized and standardized guidelines for nanoparticle testing. Nanoparticles, with their unique properties, can interfere with biochemical test methods, so multiple tests are required to fully assess their cellular effects. For a more reliable and comprehensive assessment, it is therefore imperative to include methods in nanoparticle testing routines that are not affected by particles and allow for the efficient integration of additional molecular techniques into the workflow. Digital holographic microscopy (DHM), an interferometric variant of quantitative phase imaging (QPI), has been demonstrated as a promising method for the label-free assessment of the cytotoxic potential of nanoparticles. Due to minimal interactions with the sample, DHM allows for further downstream analyses. In this study, we investigated the capabilities of DHM in a multimodal approach to assess cytotoxicity by directly comparing DHM-detected effects on the same cell population with two downstream biochemical assays. Therefore, the dry mass increase in RAW 264.7 macrophages and NIH-3T3 fibroblast populations measured by quantitative DHM phase contrast after incubation with poly(alkyl cyanoacrylate) nanoparticles for 24 h was compared to the cytotoxic control digitonin, and cell culture medium control. Viability was then determined using a metabolic activity assay (WST-8). Moreover, to determine cell death, supernatants were analyzed for the release of the enzyme lactate dehydrogenase (LDH assay). In a comparative analysis, in which the average half-maximal effective concentration (EC50) of the nanocarriers on the cells was determined, DHM was more sensitive to the effect of the nanoparticles on the used cell lines compared to the biochemical assays.
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Affiliation(s)
- Anne Marzi
- Biomedical Technology Center, University of Muenster, Mendelstraße 17, D-48149 Muenster, Germany; (K.M.E.); (Á.B.); (B.K.)
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9
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Wang S, Oliveira-Silveira J, Fang G, Kang J. High-content analysis identified synergistic drug interactions between INK128, an mTOR inhibitor, and HDAC inhibitors in a non-small cell lung cancer cell line. BMC Cancer 2024; 24:335. [PMID: 38475728 DOI: 10.1186/s12885-024-12057-4] [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: 09/07/2023] [Accepted: 02/26/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND The development of drug resistance is a major cause of cancer therapy failures. To inhibit drug resistance, multiple drugs are often treated together as a combinatorial therapy. In particular, synergistic drug combinations, which kill cancer cells at a lower concentration, guarantee a better prognosis and fewer side effects in cancer patients. Many studies have sought out synergistic combinations by small-scale function-based targeted growth assays or large-scale nontargeted growth assays, but their discoveries are always challenging due to technical problems such as a large number of possible test combinations. METHODS To address this issue, we carried out a medium-scale optical drug synergy screening in a non-small cell lung cancer cell line and further investigated individual drug interactions in combination drug responses by high-content image analysis. Optical high-content analysis of cellular responses has recently attracted much interest in the field of drug discovery, functional genomics, and toxicology. Here, we adopted a similar approach to study combinatorial drug responses. RESULTS By examining all possible combinations of 12 drug compounds in 6 different drug classes, such as mTOR inhibitors, HDAC inhibitors, HSP90 inhibitors, MT inhibitors, DNA inhibitors, and proteasome inhibitors, we successfully identified synergism between INK128, an mTOR inhibitor, and HDAC inhibitors, which has also been reported elsewhere. Our high-content analysis further showed that HDAC inhibitors, HSP90 inhibitors, and proteasome inhibitors played a dominant role in combinatorial drug responses when they were mixed with MT inhibitors, DNA inhibitors, or mTOR inhibitors, suggesting that recessive drugs could be less prioritized as components of multidrug cocktails. CONCLUSIONS In conclusion, our optical drug screening platform efficiently identified synergistic drug combinations in a non-small cell lung cancer cell line, and our high-content analysis further revealed how individual drugs in the drug mix interact with each other to generate combinatorial drug response.
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Affiliation(s)
- Sijiao Wang
- School of Chemistry and Molecular Engineering at East China Normal University, Shanghai, 200062, China
| | - Juliano Oliveira-Silveira
- Center of Biotechnology, PPGBCM, Federal University of Rio Grande Do Sul (UFRGS), Porto Alegre, Rio Grande Do Sul, 91501970, Brazil
| | - Gang Fang
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, China
- Arts and Science, New York University at Shanghai, Shanghai, 200122, China
| | - Jungseog Kang
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, China.
- Arts and Science, New York University at Shanghai, Shanghai, 200122, China.
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10
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Hua X, Han K, Mandracchia B, Radmand A, Liu W, Kim H, Yuan Z, Ehrlich SM, Li K, Zheng C, Son J, Silva Trenkle AD, Kwong GA, Zhu C, Dahlman JE, Jia S. Light-field flow cytometry for high-resolution, volumetric and multiparametric 3D single-cell analysis. Nat Commun 2024; 15:1975. [PMID: 38438356 PMCID: PMC10912605 DOI: 10.1038/s41467-024-46250-7] [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: 04/19/2023] [Accepted: 02/15/2024] [Indexed: 03/06/2024] Open
Abstract
Imaging flow cytometry (IFC) combines flow cytometry and fluorescence microscopy to enable high-throughput, multiparametric single-cell analysis with rich spatial details. However, current IFC techniques remain limited in their ability to reveal subcellular information with a high 3D resolution, throughput, sensitivity, and instrumental simplicity. In this study, we introduce a light-field flow cytometer (LFC), an IFC system capable of high-content, single-shot, and multi-color acquisition of up to 5,750 cells per second with a near-diffraction-limited resolution of 400-600 nm in all three dimensions. The LFC system integrates optical, microfluidic, and computational strategies to facilitate the volumetric visualization of various 3D subcellular characteristics through convenient access to commonly used epi-fluorescence platforms. We demonstrate the effectiveness of LFC in assaying, analyzing, and enumerating intricate subcellular morphology, function, and heterogeneity using various phantoms and biological specimens. The advancement offered by the LFC system presents a promising methodological pathway for broad cell biological and translational discoveries, with the potential for widespread adoption in biomedical research.
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Affiliation(s)
- Xuanwen Hua
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Keyi Han
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Biagio Mandracchia
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Afsane Radmand
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA
- Department of Chemical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Wenhao Liu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Hyejin Kim
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Zhou Yuan
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA
- Georgia W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Samuel M Ehrlich
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA
- Georgia W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Kaitao Li
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Corey Zheng
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Jeonghwan Son
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Aaron D Silva Trenkle
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Gabriel A Kwong
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - Cheng Zhu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - James E Dahlman
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - Shu Jia
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA.
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11
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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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12
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Xu F, Wu Z, Tan C, Liao Y, Wang Z, Chen K, Pan A. Fourier Ptychographic Microscopy 10 Years on: A Review. Cells 2024; 13:324. [PMID: 38391937 PMCID: PMC10887115 DOI: 10.3390/cells13040324] [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: 12/15/2023] [Revised: 01/31/2024] [Accepted: 02/08/2024] [Indexed: 02/24/2024] Open
Abstract
Fourier ptychographic microscopy (FPM) emerged as a prominent imaging technique in 2013, attracting significant interest due to its remarkable features such as precise phase retrieval, expansive field of view (FOV), and superior resolution. Over the past decade, FPM has become an essential tool in microscopy, with applications in metrology, scientific research, biomedicine, and inspection. This achievement arises from its ability to effectively address the persistent challenge of achieving a trade-off between FOV and resolution in imaging systems. It has a wide range of applications, including label-free imaging, drug screening, and digital pathology. In this comprehensive review, we present a concise overview of the fundamental principles of FPM and compare it with similar imaging techniques. In addition, we present a study on achieving colorization of restored photographs and enhancing the speed of FPM. Subsequently, we showcase several FPM applications utilizing the previously described technologies, with a specific focus on digital pathology, drug screening, and three-dimensional imaging. We thoroughly examine the benefits and challenges associated with integrating deep learning and FPM. To summarize, we express our own viewpoints on the technological progress of FPM and explore prospective avenues for its future developments.
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Affiliation(s)
- Fannuo Xu
- State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China; (F.X.); (Z.W.); (C.T.); (Y.L.); (Z.W.); (K.C.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zipei Wu
- State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China; (F.X.); (Z.W.); (C.T.); (Y.L.); (Z.W.); (K.C.)
- School of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
| | - Chao Tan
- State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China; (F.X.); (Z.W.); (C.T.); (Y.L.); (Z.W.); (K.C.)
- School of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
| | - Yizheng Liao
- State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China; (F.X.); (Z.W.); (C.T.); (Y.L.); (Z.W.); (K.C.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhiping Wang
- State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China; (F.X.); (Z.W.); (C.T.); (Y.L.); (Z.W.); (K.C.)
- School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China
| | - Keru Chen
- State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China; (F.X.); (Z.W.); (C.T.); (Y.L.); (Z.W.); (K.C.)
- School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - An Pan
- State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China; (F.X.); (Z.W.); (C.T.); (Y.L.); (Z.W.); (K.C.)
- University of Chinese Academy of Sciences, Beijing 100049, China
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13
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Burgess J, Nirschl JJ, Zanellati MC, Lozano A, Cohen S, Yeung-Levy S. Orientation-invariant autoencoders learn robust representations for shape profiling of cells and organelles. Nat Commun 2024; 15:1022. [PMID: 38310122 PMCID: PMC10838319 DOI: 10.1038/s41467-024-45362-4] [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: 07/09/2023] [Accepted: 01/19/2024] [Indexed: 02/05/2024] Open
Abstract
Cell and organelle shape are driven by diverse genetic and environmental factors and thus accurate quantification of cellular morphology is essential to experimental cell biology. Autoencoders are a popular tool for unsupervised biological image analysis because they learn a low-dimensional representation that maps images to feature vectors to generate a semantically meaningful embedding space of morphological variation. The learned feature vectors can also be used for clustering, dimensionality reduction, outlier detection, and supervised learning problems. Shape properties do not change with orientation, and thus we argue that representation learning methods should encode this orientation invariance. We show that conventional autoencoders are sensitive to orientation, which can lead to suboptimal performance on downstream tasks. To address this, we develop O2-variational autoencoder (O2-VAE), an unsupervised method that learns robust, orientation-invariant representations. We use O2-VAE to discover morphology subgroups in segmented cells and mitochondria, detect outlier cells, and rapidly characterise cellular shape and texture in large datasets, including in a newly generated synthetic benchmark.
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Affiliation(s)
- James Burgess
- Institute for Computational & Mathematical Engineering, Stanford University, Stanford, CA, USA.
| | - Jeffrey J Nirschl
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Maria-Clara Zanellati
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alejandro Lozano
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Sarah Cohen
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Serena Yeung-Levy
- Departments of Biomedical Data Science, Computer Science, and Electrical Engineering, Stanford University, Stanford, CA, USA.
- Chan Zuckerberg Biohub - San Francisco, San Francisco, CA, USA.
- Clinical Excellence Research Center, School of Medicine, Stanford University, Stanford, CA, USA.
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14
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Zhang R, Huang Y, Li M, Wang L, Li B, Xia A, Li Y, Yang S, Jin F. High-throughput, microscopy-based screening and quantification of genetic elements. MLIFE 2023; 2:450-461. [PMID: 38818273 PMCID: PMC10989126 DOI: 10.1002/mlf2.12096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 09/08/2023] [Accepted: 10/10/2023] [Indexed: 06/01/2024]
Abstract
Synthetic biology relies on the screening and quantification of genetic components to assemble sophisticated gene circuits with specific functions. Microscopy is a powerful tool for characterizing complex cellular phenotypes with increasing spatial and temporal resolution to library screening of genetic elements. Microscopy-based assays are powerful tools for characterizing cellular phenotypes with spatial and temporal resolution and can be applied to large-scale samples for library screening of genetic elements. However, strategies for high-throughput microscopy experiments remain limited. Here, we present a high-throughput, microscopy-based platform that can simultaneously complete the preparation of an 8 × 12-well agarose pad plate, allowing for the screening of 96 independent strains or experimental conditions in a single experiment. Using this platform, we screened a library of natural intrinsic promoters from Pseudomonas aeruginosa and identified a small subset of robust promoters that drives stable levels of gene expression under varying growth conditions. Additionally, the platform allowed for single-cell measurement of genetic elements over time, enabling the identification of complex and dynamic phenotypes to map genotype in high throughput. We expected that the platform could be employed to accelerate the identification and characterization of genetic elements in various biological systems, as well as to understand the relationship between cellular phenotypes and internal states, including genotypes and gene expression programs.
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Affiliation(s)
- Rongrong Zhang
- CAS Key Laboratory of Quantitative Engineering BiologyShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Yajia Huang
- CAS Key Laboratory of Quantitative Engineering BiologyShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Mei Li
- CAS Key Laboratory of Quantitative Engineering BiologyShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Lei Wang
- Shenzhen Synthetic Biology InfrastructureShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Bing Li
- CAS Key Laboratory of Quantitative Engineering BiologyShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Aiguo Xia
- Shenzhen Synthetic Biology InfrastructureShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Ye Li
- Shenzhen Synthetic Biology InfrastructureShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Shuai Yang
- CAS Key Laboratory of Quantitative Engineering BiologyShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
- Chengdu Documentation and Information CenterChinese Academy of SciencesChengduChina
| | - Fan Jin
- CAS Key Laboratory of Quantitative Engineering BiologyShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
- Shenzhen Synthetic Biology InfrastructureShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
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15
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Keikhosravi A, Almansour F, Bohrer CH, Fursova NA, Guin K, Sood V, Misteli T, Larson DR, Pegoraro G. HiTIPS: High-Throughput Image Processing Software for the Study of Nuclear Architecture and Gene Expression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.02.565366. [PMID: 38076967 PMCID: PMC10705580 DOI: 10.1101/2023.11.02.565366] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/14/2024]
Abstract
High-throughput imaging (HTI) generates complex imaging datasets from a large number of experimental perturbations. Commercial HTI software for image analysis workflows does not allow full customization and adoption of new image processing algorithms in the analysis modules. While open-source HTI analysis platforms provide individual modules in the workflow, like nuclei segmentation, spot detection, or cell tracking, they are often limited in integrating novel analysis modules or algorithms. Here, we introduce the High-Throughput Image Processing Software (HiTIPS) to expand the range and customization of existing HTI analysis capabilities. HiTIPS incorporates advanced image processing and machine learning algorithms for automated cell and nuclei segmentation, spot signal detection, nucleus tracking, spot tracking, and quantification of spot signal intensity. Furthermore, HiTIPS features a graphical user interface that is open to integration of new algorithms for existing analysis pipelines and to adding new analysis pipelines through separate plugins. To demonstrate the utility of HiTIPS, we present three examples of image analysis workflows for high-throughput DNA FISH, immunofluorescence (IF), and live-cell imaging of transcription in single cells. Altogether, we demonstrate that HiTIPS is a user-friendly, flexible, and open-source HTI analysis platform for a variety of cell biology applications.
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16
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Antonelli L, Polverino F, Albu A, Hada A, Asteriti IA, Degrassi F, Guarguaglini G, Maddalena L, Guarracino MR. ALFI: Cell cycle phenotype annotations of label-free time-lapse imaging data from cultured human cells. Sci Data 2023; 10:677. [PMID: 37794110 PMCID: PMC10551030 DOI: 10.1038/s41597-023-02540-1] [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: 02/21/2023] [Accepted: 09/05/2023] [Indexed: 10/06/2023] Open
Abstract
Detecting and tracking multiple moving objects in a video is a challenging task. For living cells, the task becomes even more arduous as cells change their morphology over time, can partially overlap, and mitosis leads to new cells. Differently from fluorescence microscopy, label-free techniques can be easily applied to almost all cell lines, reducing sample preparation complexity and phototoxicity. In this study, we present ALFI, a dataset of images and annotations for label-free microscopy, made publicly available to the scientific community, that notably extends the current panorama of expertly labeled data for detection and tracking of cultured living nontransformed and cancer human cells. It consists of 29 time-lapse image sequences from HeLa, U2OS, and hTERT RPE-1 cells under different experimental conditions, acquired by differential interference contrast microscopy, for a total of 237.9 hours. It contains various annotations (pixel-wise segmentation masks, object-wise bounding boxes, tracking information). The dataset is useful for testing and comparing methods for identifying interphase and mitotic events and reconstructing their lineage, and for discriminating different cellular phenotypes.
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Affiliation(s)
- Laura Antonelli
- ICAR, Institute for High-Performance Computing and Networking, National Research Council, Naples, Italy
| | - Federica Polverino
- IBPM, Institute of Molecular Biology and Pathology, National Research Council, Rome, Italy
| | - Alexandra Albu
- Department of Economics and Law, University of Cassino and Southern Lazio, Cassino, Italy
| | - Aroj Hada
- Department of Economics and Law, University of Cassino and Southern Lazio, Cassino, Italy
| | - Italia A Asteriti
- IBPM, Institute of Molecular Biology and Pathology, National Research Council, Rome, Italy
| | - Francesca Degrassi
- IBPM, Institute of Molecular Biology and Pathology, National Research Council, Rome, Italy
| | - Giulia Guarguaglini
- IBPM, Institute of Molecular Biology and Pathology, National Research Council, Rome, Italy.
| | - Lucia Maddalena
- ICAR, Institute for High-Performance Computing and Networking, National Research Council, Naples, Italy.
| | - Mario R Guarracino
- Department of Economics and Law, University of Cassino and Southern Lazio, Cassino, Italy
- Laboratory of Algorithms and Technologies for Networks Analysis, National Research University Higher School of Economics, Moscow, Russia
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17
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Soha SA, Santhireswaran A, Huq S, Casimir-Powell J, Jenkins N, Hodgson GK, Sugiyama M, Antonescu CN, Impellizzeri S, Botelho RJ. Improved imaging and preservation of lysosome dynamics using silver nanoparticle-enhanced fluorescence. Mol Biol Cell 2023; 34:ar96. [PMID: 37405751 PMCID: PMC10551705 DOI: 10.1091/mbc.e22-06-0200] [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: 06/07/2022] [Revised: 06/26/2023] [Accepted: 06/27/2023] [Indexed: 07/06/2023] Open
Abstract
The dynamics of living cells can be studied by live-cell fluorescence microscopy. However, this requires the use of excessive light energy to obtain good signal-to-noise ratio, which can then photobleach fluorochromes, and more worrisomely, lead to phototoxicity. Upon light excitation, noble metal nanoparticles such as silver nanoparticles (AgNPs) generate plasmons, which can then amplify excitation in direct proximity of the nanoparticle's surface and couple to the oscillating dipole of nearby radiating fluorophores, modifying their rate of emission and thus, enhancing their fluorescence. Here, we show that AgNPs fed to cells to accumulate within lysosomes enhanced the fluorescence of lysosome-targeted Alexa488-conjugated dextran, BODIPY-cholesterol, and DQ-BSA. Moreover, AgNP increased the fluorescence of GFP fused to the cytosolic tail of LAMP1, showing that metal enhanced fluorescence can occur across the lysosomal membrane. The inclusion of AgNPs in lysosomes did not disturb lysosomal properties such as lysosomal pH, degradative capacity, autophagy and autophagic flux, and membrane integrity, though AgNP seemed to increase basal lysosome tubulation. Importantly, by using AgNP, we could track lysosome motility with reduced laser power without damaging and altering lysosome dynamics. Overall, AgNP-enhanced fluorescence may be a useful tool to study the dynamics of the endo-lysosomal pathway while minimizing phototoxicity.
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Affiliation(s)
- Sumaiya A. Soha
- Molecular Science Graduate Program, Toronto Metropolitan University, Toronto, Ontario, Canada, M5B 2K3
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Ontario, Canada, M5B 2K3
| | - Araniy Santhireswaran
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Ontario, Canada, M5B 2K3
| | - Saaimatul Huq
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Ontario, Canada, M5B 2K3
| | - Jayde Casimir-Powell
- Molecular Science Graduate Program, Toronto Metropolitan University, Toronto, Ontario, Canada, M5B 2K3
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Ontario, Canada, M5B 2K3
| | - Nicala Jenkins
- Molecular Science Graduate Program, Toronto Metropolitan University, Toronto, Ontario, Canada, M5B 2K3
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Ontario, Canada, M5B 2K3
| | - Gregory K. Hodgson
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Ontario, Canada, M5B 2K3
| | - Michael Sugiyama
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Ontario, Canada, M5B 2K3
| | - Costin N. Antonescu
- Molecular Science Graduate Program, Toronto Metropolitan University, Toronto, Ontario, Canada, M5B 2K3
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Ontario, Canada, M5B 2K3
| | - Stefania Impellizzeri
- Molecular Science Graduate Program, Toronto Metropolitan University, Toronto, Ontario, Canada, M5B 2K3
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Ontario, Canada, M5B 2K3
| | - Roberto J. Botelho
- Molecular Science Graduate Program, Toronto Metropolitan University, Toronto, Ontario, Canada, M5B 2K3
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Ontario, Canada, M5B 2K3
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18
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Gerling T, Godino N, Pfisterer F, Hupf N, Kirschbaum M. High-precision, low-complexity, high-resolution microscopy-based cell sorting. LAB ON A CHIP 2023. [PMID: 37314345 DOI: 10.1039/d3lc00242j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Continuous flow cell sorting based on image analysis is a powerful concept that exploits spatially-resolved features in cells, such as subcellular protein localisation or cell and organelle morphology, to isolate highly specialised cell types that were previously inaccessible to biomedical research, biotechnology, and medicine. Recently, sorting protocols have been proposed that achieve impressive throughput by combining ultra-high flow rates with sophisticated imaging and data processing protocols. However, moderate image quality and high complex experimental setups still prevent the full potential of image-activated cell sorting from being a general-purpose tool. Here, we present a new low-complexity microfluidic approach based on high numerical aperture wide-field microscopy and precise dielectrophoretic cell handling. It provides high-quality images with unprecedented resolution in image-activated cell sorting (i.e., 216 nm). In addition, it also allows long image processing times of several hundred milliseconds for thorough image analysis, while ensuring reliable and low-loss cell processing. Using our approach, we sorted live T cells based on subcellular localisation of fluorescence signals and demonstrated that purities above 80% are possible while targeting maximum yields and sample volume throughputs in the range of μl min-1. We were able to recover 85% of the target cells analysed. Finally, we ensure and quantify the full vitality of the sorted cells cultivating the cells for a period of time and through colorimetric viability tests.
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Affiliation(s)
- Tobias Gerling
- Fraunhofer Institute for Cell Therapy and Immunology, Branch Bioanalytics and Bioprocesses IZI-BB, Am Muehlenberg 13, 14476 Potsdam, Germany.
- Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24/25, 14476 Potsdam, Germany
| | - Neus Godino
- Fraunhofer Institute for Cell Therapy and Immunology, Branch Bioanalytics and Bioprocesses IZI-BB, Am Muehlenberg 13, 14476 Potsdam, Germany.
| | - Felix Pfisterer
- Fraunhofer Institute for Cell Therapy and Immunology, Branch Bioanalytics and Bioprocesses IZI-BB, Am Muehlenberg 13, 14476 Potsdam, Germany.
| | - Nina Hupf
- Fraunhofer Institute for Cell Therapy and Immunology, Branch Bioanalytics and Bioprocesses IZI-BB, Am Muehlenberg 13, 14476 Potsdam, Germany.
| | - Michael Kirschbaum
- Fraunhofer Institute for Cell Therapy and Immunology, Branch Bioanalytics and Bioprocesses IZI-BB, Am Muehlenberg 13, 14476 Potsdam, Germany.
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19
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Hu J, Serra‐Picamal X, Bakker G, Van Troys M, Winograd‐Katz S, Ege N, Gong X, Didan Y, Grosheva I, Polansky O, Bakkali K, Van Hamme E, van Erp M, Vullings M, Weiss F, Clucas J, Dowbaj AM, Sahai E, Ampe C, Geiger B, Friedl P, Bottai M, Strömblad S. Multisite assessment of reproducibility in high-content cell migration imaging data. Mol Syst Biol 2023; 19:e11490. [PMID: 37063090 PMCID: PMC10258559 DOI: 10.15252/msb.202211490] [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: 12/02/2022] [Revised: 03/27/2023] [Accepted: 03/30/2023] [Indexed: 04/18/2023] Open
Abstract
High-content image-based cell phenotyping provides fundamental insights into a broad variety of life science disciplines. Striving for accurate conclusions and meaningful impact demands high reproducibility standards, with particular relevance for high-quality open-access data sharing and meta-analysis. However, the sources and degree of biological and technical variability, and thus the reproducibility and usefulness of meta-analysis of results from live-cell microscopy, have not been systematically investigated. Here, using high-content data describing features of cell migration and morphology, we determine the sources of variability across different scales, including between laboratories, persons, experiments, technical repeats, cells, and time points. Significant technical variability occurred between laboratories and, to lesser extent, between persons, providing low value to direct meta-analysis on the data from different laboratories. However, batch effect removal markedly improved the possibility to combine image-based datasets of perturbation experiments. Thus, reproducible quantitative high-content cell image analysis of perturbation effects and meta-analysis depend on standardized procedures combined with batch correction.
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Affiliation(s)
- Jianjiang Hu
- Department of Biosciences and NutritionKarolinska InstitutetStockholmSweden
| | | | - Gert‐Jan Bakker
- Department of Medical BioSciencesRadboud University Medical CenterNijmegenThe Netherlands
| | | | - Sabina Winograd‐Katz
- Department of Immunology and Regenerative BiologyWeizmann Institute of ScienceRehovotIsrael
| | - Nil Ege
- The Francis Crick InstituteLondonUK
| | - Xiaowei Gong
- Department of Biosciences and NutritionKarolinska InstitutetStockholmSweden
| | - Yuliia Didan
- Department of Biosciences and NutritionKarolinska InstitutetStockholmSweden
| | - Inna Grosheva
- Department of Immunology and Regenerative BiologyWeizmann Institute of ScienceRehovotIsrael
| | - Omer Polansky
- Department of Immunology and Regenerative BiologyWeizmann Institute of ScienceRehovotIsrael
| | - Karima Bakkali
- Department of Biomolecular MedicineGhent UniversityGhentBelgium
| | | | - Merijn van Erp
- Department of Medical BioSciencesRadboud University Medical CenterNijmegenThe Netherlands
| | - Manon Vullings
- Department of Medical BioSciencesRadboud University Medical CenterNijmegenThe Netherlands
| | - Felix Weiss
- Department of Medical BioSciencesRadboud University Medical CenterNijmegenThe Netherlands
| | | | | | | | - Christophe Ampe
- Department of Biomolecular MedicineGhent UniversityGhentBelgium
| | - Benjamin Geiger
- Department of Immunology and Regenerative BiologyWeizmann Institute of ScienceRehovotIsrael
| | - Peter Friedl
- Department of Medical BioSciencesRadboud University Medical CenterNijmegenThe Netherlands
| | - Matteo Bottai
- Division of Biostatistics, Institute of Environmental MedicineKarolinska InstitutetStockholmSweden
| | - Staffan Strömblad
- Department of Biosciences and NutritionKarolinska InstitutetStockholmSweden
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20
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Tanaka M, Thoma J, Poisa-Beiro L, Wuchter P, Eckstein V, Dietrich S, Pabst C, Müller-Tidow C, Ohta T, Ho AD. Physical biomarkers for human hematopoietic stem and progenitor cells. Cells Dev 2023; 174:203845. [PMID: 37116713 DOI: 10.1016/j.cdev.2023.203845] [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: 02/13/2023] [Revised: 04/18/2023] [Accepted: 04/21/2023] [Indexed: 04/30/2023]
Abstract
Adhesion of hematopoietic stem and progenitor cells (HSPCs) to the bone marrow niche plays critical roles in the maintenance of the most primitive HSPCs. The interactions of HSPC-niche interactions are clinically relevant in acute myeloid leukemia (AML), because (i) leukemia-initiating cells adhered to the marrow niche are protected from the cytotoxic effect by chemotherapy and (ii) mobilization of HSPCs from healthy donors' bone marrow is crucial for the effective stem cell transplantation. However, although many clinical agents have been developed for the HSPC mobilization, the effects caused by the extrinsic molecular cues were traditionally evaluated based on phenomenological observations. This review highlights the recent interdisciplinary challenges of hematologists, biophysicists and cell biologists towards the design of defined in vitro niche models and the development of physical biomarkers for quantitative indexing of differential effects of clinical agents on human HSPCs.
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Affiliation(s)
- Motomu Tanaka
- Physical Chemistry of Biosystems, Institute of Physical Chemistry, INF253, Heidelberg University, 69120 Heidelberg, Germany; Center for Integrative Medicine and Physics, Institute for Advanced Study, Kyoto University, 606-8501 Kyoto, Japan.
| | - Judith Thoma
- Physical Chemistry of Biosystems, Institute of Physical Chemistry, INF253, Heidelberg University, 69120 Heidelberg, Germany
| | - Laura Poisa-Beiro
- Department of Medicine V, Heidelberg University, INF410, 69120 Heidelberg, Germany
| | - Patrick Wuchter
- Department of Medicine V, Heidelberg University, INF410, 69120 Heidelberg, Germany
| | - Volker Eckstein
- Department of Medicine V, Heidelberg University, INF410, 69120 Heidelberg, Germany
| | - Sascha Dietrich
- Department of Medicine V, Heidelberg University, INF410, 69120 Heidelberg, Germany
| | - Caroline Pabst
- Department of Medicine V, Heidelberg University, INF410, 69120 Heidelberg, Germany
| | - Carsten Müller-Tidow
- Department of Medicine V, Heidelberg University, INF410, 69120 Heidelberg, Germany
| | - Takao Ohta
- Center for Integrative Medicine and Physics, Institute for Advanced Study, Kyoto University, 606-8501 Kyoto, Japan
| | - Anthony D Ho
- Center for Integrative Medicine and Physics, Institute for Advanced Study, Kyoto University, 606-8501 Kyoto, Japan; Department of Medicine V, Heidelberg University, INF410, 69120 Heidelberg, Germany; Molecular Medicine Partnership Unit Heidelberg, European Molecular Biology Laboratory (EMBL), Heidelberg University, 69120 Heidelberg, Germany.
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21
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André O, Kumra Ahnlide J, Norlin N, Swaminathan V, Nordenfelt P. Data-driven microscopy allows for automated context-specific acquisition of high-fidelity image data. CELL REPORTS METHODS 2023; 3:100419. [PMID: 37056378 PMCID: PMC10088093 DOI: 10.1016/j.crmeth.2023.100419] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 12/20/2022] [Accepted: 02/10/2023] [Indexed: 04/15/2023]
Abstract
Light microscopy is a powerful single-cell technique that allows for quantitative spatial information at subcellular resolution. However, unlike flow cytometry and single-cell sequencing techniques, microscopy has issues achieving high-quality population-wide sample characterization while maintaining high resolution. Here, we present a general framework, data-driven microscopy (DDM) that uses real-time population-wide object characterization to enable data-driven high-fidelity imaging of relevant phenotypes based on the population context. DDM combines data-independent and data-dependent steps to synergistically enhance data acquired using different imaging modalities. As a proof of concept, we develop and apply DDM with plugins for improved high-content screening and live adaptive microscopy for cell migration and infection studies that capture events of interest, rare or common, with high precision and resolution. We propose that DDM can reduce human bias, increase reproducibility, and place single-cell characteristics in the context of the sample population when interpreting microscopy data, leading to an increase in overall data fidelity.
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Affiliation(s)
- Oscar André
- Department of Clinical Sciences, Faculty of Medicine, Lund University, Lund, Sweden
| | | | - Nils Norlin
- Department of Experimental Medical Science, Lund University, Lund, Sweden
- Lund University Bioimaging Centre, Lund, Sweden
| | - Vinay Swaminathan
- Department of Clinical Sciences, Wallenberg Centre for Molecular Medicine, Division of Oncology, Lund University, Lund, Sweden
| | - Pontus Nordenfelt
- Department of Clinical Sciences, Faculty of Medicine, Lund University, Lund, Sweden
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22
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Zhao X, Ding L, Yan J, Xu J, He H. Constructing an In Vitro and In Vivo Flow Cytometry by Fast Line Scanning of Confocal Microscopy. SENSORS (BASEL, SWITZERLAND) 2023; 23:3305. [PMID: 36992015 PMCID: PMC10059927 DOI: 10.3390/s23063305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 03/15/2023] [Accepted: 03/19/2023] [Indexed: 06/19/2023]
Abstract
Composed of a fluidic and an optical system, flow cytometry has been widely used for biosensing. The fluidic flow enables its automatic high-throughput sample loading and sorting while the optical system works for molecular detection by fluorescence for micron-level cells and particles. This technology is quite powerful and highly developed; however, it requires a sample in the form of a suspension and thus only works in vitro. In this study, we report a simple scheme to construct a flow cytometry based on a confocal microscope without any modifications. We demonstrate that line scanning of microscopy can effectively excite fluorescence of flowing microbeads or cells in a capillary tube in vitro and in blood vessels of live mice in vivo. This method can resolve microbeads at several microns and the results are comparable to a classic flow cytometer. The absolute diameter of flowing samples can be indicated directly. The sampling limitations and variations of this method is carefully analyzed. This scheme can be easily accomplished by any commercial confocal microscope systems, expands the function of them, and is of promising potential for simultaneous confocal microscopy and in vivo detection of cells in blood vessels of live animals by a single system.
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Affiliation(s)
- Xiaohui Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (X.Z.)
| | - Leqi Ding
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; (L.D.)
| | - Jingsheng Yan
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; (L.D.)
| | - Jin Xu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (X.Z.)
| | - Hao He
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (X.Z.)
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23
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Gong J, Jin Z, Chen H, He J, Zhang Y, Yang X. Super-resolution fluorescence microscopic imaging in pathogenesis and drug treatment of neurological disease. Adv Drug Deliv Rev 2023; 196:114791. [PMID: 37004939 DOI: 10.1016/j.addr.2023.114791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 03/16/2023] [Accepted: 03/19/2023] [Indexed: 04/03/2023]
Abstract
Since super-resolution fluorescence microscopic technology breaks the diffraction limit that has existed for a long time in optical imaging, it can observe the process of synapses formed between nerve cells and the protein aggregation related to neurological disease. Thus, super-resolution fluorescence microscopic imaging has significantly impacted several industries, including drug development and pathogenesis research, and it is anticipated that it will significantly alter the future of life science research. Here, we focus on several typical super-resolution fluorescence microscopic technologies, introducing their benefits and drawbacks, as well as applications in several common neurological diseases, in the hope that their services will be expanded and improved in the pathogenesis and drug treatment of neurological diseases.
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24
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Papandreou A, Luft C, Barral S, Kriston-Vizi J, Kurian MA, Ketteler R. Automated high-content imaging in iPSC-derived neuronal progenitors. SLAS DISCOVERY : ADVANCING LIFE SCIENCES R & D 2023; 28:42-51. [PMID: 36610640 PMCID: PMC10602900 DOI: 10.1016/j.slasd.2022.12.002] [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: 08/22/2022] [Revised: 12/18/2022] [Accepted: 12/31/2022] [Indexed: 01/07/2023]
Abstract
Induced pluripotent stem cells (iPSCs) have great potential as physiological disease models for human disorders where access to primary cells is difficult, such as neurons. In recent years, many protocols have been developed for the generation of iPSCs and the differentiation into specialised cell subtypes of interest. More recently, these models have been modified to allow large-scale phenotyping and high-content screening of small molecule compounds in iPSC-derived neuronal cells. Here, we describe the automated seeding of day 11 ventral midbrain progenitor cells into 96-well plates, administration of compounds, automated staining for immunofluorescence, the acquisition of images on a high-content screening platform and workflows for image analysis.
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Affiliation(s)
- Apostolos Papandreou
- University College London MRC Laboratory for Molecular Cell Biology, London, UK; Developmental Neurosciences, Zayed Centre for Research into Rare Disease in Children, University College London Great Ormond Street Institute of Child Health, London, UK
| | - Christin Luft
- University College London MRC Laboratory for Molecular Cell Biology, London, UK
| | - Serena Barral
- Developmental Neurosciences, Zayed Centre for Research into Rare Disease in Children, University College London Great Ormond Street Institute of Child Health, London, UK
| | - Janos Kriston-Vizi
- University College London MRC Laboratory for Molecular Cell Biology, London, UK
| | - Manju A Kurian
- Developmental Neurosciences, Zayed Centre for Research into Rare Disease in Children, University College London Great Ormond Street Institute of Child Health, London, UK
| | - Robin Ketteler
- University College London MRC Laboratory for Molecular Cell Biology, London, UK
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25
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Ha D, Kong J, Kim D, Lee K, Lee J, Park M, Ahn H, Oh Y, Kim S. Development of bioinformatics and multi-omics analyses in organoids. BMB Rep 2023; 56:43-48. [PMID: 36284440 PMCID: PMC9887100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Indexed: 01/28/2023] Open
Abstract
Pre-clinical models are critical in gaining mechanistic and biological insights into disease progression. Recently, patient-derived organoid models have been developed to facilitate our understanding of disease development and to improve the discovery of therapeutic options by faithfully recapitulating in vivo tissues or organs. As technological developments of organoid models are rapidly growing, computational methods are gaining attention in organoid researchers to improve the ability to systematically analyze experimental results. In this review, we summarize the recent advances in organoid models to recapitulate human diseases and computational advancements to analyze experimental results from organoids. [BMB Reports 2023; 56(1): 43-48].
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Affiliation(s)
- Doyeon Ha
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - JungHo Kong
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Donghyo Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Kwanghwan Lee
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Juhun Lee
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Minhyuk Park
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Hyunsoo Ahn
- Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Youngchul Oh
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Sanguk Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea,Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang 37673, Korea,Corresponding author. Tel: +82-54-279-2348; Fax: +82-54-279-2199; E-mail:
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26
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Chen S, Ding C, Liu M, Cheng J, Tao D. CPP-Net: Context-aware Polygon Proposal Network for Nucleus Segmentation. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2023; PP:980-994. [PMID: 37022023 DOI: 10.1109/tip.2023.3237013] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Nucleus segmentation is a challenging task due to the crowded distribution and blurry boundaries of nuclei. To differentiate between touching and overlapping nuclei, recent approaches have represented nuclei in the form of polygons, and have accordingly achieved promising performance. Each polygon is represented by a set of centroid-to-boundary distances, which are in turn predicted by features of the centroid pixel for a single nucleus. However, the use of the centroid pixel alone does not provide sufficient contextual information for robust prediction and therefore affects the segmentation accuracy. To address this problem, we propose a Context-aware Polygon Proposal Network (CPP-Net) for nucleus segmentation. First, we sample a point set rather than a single pixel within each cell for distance prediction; this strategy substantially enhances the contextual information and thereby improves the prediction robustness. Second, we propose a Confidence-basedWeighting Module, which adaptively fuses the predictions from the sampled point set. Third, we introduce a novel Shape-Aware Perceptual (SAP) loss that constrains the shape of the predicted polygons. This SAP loss is based on an additional network that is pre-trained by means of mapping the centroid probability map and the pixel-to-boundary distance maps to a different nucleus representation. Extensive experiments demonstrate the effectiveness of each component in the proposed CPP-Net. Finally, CPP-Net is found to achieve state-of-the-art performance on three publicly available databases, namely DSB2018, BBBC06, and PanNuke. The code of this paper will be released.
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27
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Pelicci S, Furia L, Pelicci PG, Faretta M. Correlative Multi-Modal Microscopy: A Novel Pipeline for Optimizing Fluorescence Microscopy Resolutions in Biological Applications. Cells 2023; 12:cells12030354. [PMID: 36766696 PMCID: PMC9913119 DOI: 10.3390/cells12030354] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/05/2023] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
The modern fluorescence microscope is the convergence point of technologies with different performances in terms of statistical sampling, number of simultaneously analyzed signals, and spatial resolution. However, the best results are usually obtained by maximizing only one of these parameters and finding a compromise for the others, a limitation that can become particularly significant when applied to cell biology and that can reduce the spreading of novel optical microscopy tools among research laboratories. Super resolution microscopy and, in particular, molecular localization-based approaches provide a spatial resolution and a molecular localization precision able to explore the scale of macromolecular complexes in situ. However, its use is limited to restricted regions, and consequently few cells, and frequently no more than one or two parameters. Correlative microscopy, obtained by the fusion of different optical technologies, can consequently surpass this barrier by merging results from different spatial scales. We discuss here the use of an acquisition and analysis correlative microscopy pipeline to obtain high statistical sampling, high content, and maximum spatial resolution by combining widefield, confocal, and molecular localization microscopy.
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Affiliation(s)
- Simone Pelicci
- Department of Experimental Oncology, European Institute of Oncology IRCCS, 20139 Milan, Italy
| | - Laura Furia
- Department of Experimental Oncology, European Institute of Oncology IRCCS, 20139 Milan, Italy
| | - Pier Giuseppe Pelicci
- Department of Experimental Oncology, European Institute of Oncology IRCCS, 20139 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Mario Faretta
- Department of Experimental Oncology, European Institute of Oncology IRCCS, 20139 Milan, Italy
- Correspondence:
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28
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Krentzel D, Shorte SL, Zimmer C. Deep learning in image-based phenotypic drug discovery. Trends Cell Biol 2023:S0962-8924(22)00262-8. [PMID: 36623998 DOI: 10.1016/j.tcb.2022.11.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 11/26/2022] [Accepted: 11/29/2022] [Indexed: 01/08/2023]
Abstract
Modern drug discovery approaches often use high-content imaging to systematically study the effect on cells of large libraries of chemical compounds. By automatically screening thousands or millions of images to identify specific drug-induced cellular phenotypes, for example, altered cellular morphology, these approaches can reveal 'hit' compounds offering therapeutic promise. In the past few years, artificial intelligence (AI) methods based on deep learning (DL) [a family of machine learning (ML) techniques] have disrupted virtually all image analysis tasks, from image classification to segmentation. These powerful methods also promise to impact drug discovery by accelerating the identification of effective drugs and their modes of action. In this review, we highlight applications and adaptations of ML, especially DL methods for cell-based phenotypic drug discovery (PDD).
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Affiliation(s)
- Daniel Krentzel
- Institut Pasteur, Université Paris Cité, Imaging and Modeling Unit, F-75015 Paris, France; Institut Pasteur, Joint International Unit Artificial Intelligence for Image-based Drug Discovery & Development (PIU-Ai3D), F-75015 Paris, France.
| | - Spencer L Shorte
- Institut Pasteur, Joint International Unit Artificial Intelligence for Image-based Drug Discovery & Development (PIU-Ai3D), F-75015 Paris, France; Institut Pasteur, Université Paris Cité, Photonic Bio-Imaging, Centre de Ressources et Recherches Technologiques (UTechS-PBI, C2RT), F-75015 Paris, France
| | - Christophe Zimmer
- Institut Pasteur, Université Paris Cité, Imaging and Modeling Unit, F-75015 Paris, France; Institut Pasteur, Joint International Unit Artificial Intelligence for Image-based Drug Discovery & Development (PIU-Ai3D), F-75015 Paris, France.
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29
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Grohme MA, Frank O, Rink JC. Preparing Planarian Cells for High-Content Fluorescence Microscopy Using RNA in Situ Hybridization and Immunocytochemistry. Methods Mol Biol 2023; 2680:121-155. [PMID: 37428375 DOI: 10.1007/978-1-0716-3275-8_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
High-content fluorescence microscopy combines the efficiency of high-throughput techniques with the ability to extract quantitative information from biological systems. Here we describe a modular collection of assays adapted for fixed planarian cells that enable multiplexed measurements of biomarkers in microwell plates. These include protocols for RNA fluorescent in situ hybridization (RNA FISH) as well as immunocytochemical protocols for quantifying proliferating cells targeting phosphorylated histone H3 as well as 5-bromo-2'-deoxyuridine (BrdU) incorporated into the nuclear DNA. The assays are compatible with planarians of virtually any size, as the tissue is disaggregated into a single-cell suspension before fixation and staining. By sharing many reagents with established planarian whole-mount staining protocols, preparation of samples for high-content microscopy adoption requires little additional investment.
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Affiliation(s)
- Markus A Grohme
- Max Planck Institute for Molecular Cell Biology and Genetics, Dresden, Germany
| | - Olga Frank
- Max Planck Institute for Molecular Cell Biology and Genetics, Dresden, Germany
| | - Jochen C Rink
- Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany.
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30
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Baručić D, Kaushik S, Kybic J, Stanková J, Džubák P, Hajdúch M. Characterization of drug effects on cell cultures from phase-contrast microscopy images. Comput Biol Med 2022; 151:106171. [PMID: 36306582 DOI: 10.1016/j.compbiomed.2022.106171] [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: 05/26/2022] [Revised: 08/30/2022] [Accepted: 10/01/2022] [Indexed: 12/27/2022]
Abstract
In this work, we classify chemotherapeutic agents (topoisomerase inhibitors) based on their effect on U-2 OS cells. We use phase-contrast microscopy images, which are faster and easier to obtain than fluorescence images and support live cell imaging. We use a convolutional neural network (CNN) trained end-to-end directly on the input images without requiring for manual segmentations or any other auxiliary data. Our method can distinguish between tested cytotoxic drugs with an accuracy of 98%, provided that their mechanism of action differs, outperforming previous work. The results are even better when substance-specific concentrations are used. We show the benefit of sharing the extracted features over all classes (drugs). Finally, a 2D visualization of these features reveals clusters, which correspond well to known class labels, suggesting the possible use of our methodology for drug discovery application in analyzing new, unseen drugs.
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Affiliation(s)
- Denis Baručić
- Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic.
| | - Sumit Kaushik
- Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic.
| | - Jan Kybic
- Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic.
| | - Jarmila Stanková
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University, Olomouc, Czech Republic
| | - Petr Džubák
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University, Olomouc, Czech Republic
| | - Marián Hajdúch
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University, Olomouc, Czech Republic
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31
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A cellular segmentation algorithm with fast customization. Nat Methods 2022; 19:1536-1537. [PMID: 36344836 DOI: 10.1038/s41592-022-01664-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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32
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Mills CE, Subramanian K, Hafner M, Niepel M, Gerosa L, Chung M, Victor C, Gaudio B, Yapp C, Nirmal AJ, Clark N, Sorger PK. Multiplexed and reproducible high content screening of live and fixed cells using Dye Drop. Nat Commun 2022; 13:6918. [PMID: 36376301 PMCID: PMC9663587 DOI: 10.1038/s41467-022-34536-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 10/06/2022] [Indexed: 11/16/2022] Open
Abstract
High-throughput measurement of cells perturbed using libraries of small molecules, gene knockouts, or different microenvironmental factors is a key step in functional genomics and pre-clinical drug discovery. However, it remains difficult to perform accurate single-cell assays in 384-well plates, limiting many studies to well-average measurements (e.g., CellTiter-Glo®). Here we describe a public domain Dye Drop method that uses sequential density displacement and microscopy to perform multi-step assays on living cells. We use Dye Drop cell viability and DNA replication assays followed by immunofluorescence imaging to collect single-cell dose-response data for 67 investigational and clinical-grade small molecules in 58 breast cancer cell lines. By separating the cytostatic and cytotoxic effects of drugs computationally, we uncover unexpected relationships between the two. Dye Drop is rapid, reproducible, customizable, and compatible with manual or automated laboratory equipment. Dye Drop improves the tradeoff between data content and cost, enabling the collection of information-rich perturbagen-response datasets.
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Affiliation(s)
- Caitlin E Mills
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Kartik Subramanian
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
- Bristol Myers Squibb, Cambridge, MA, 02142, USA
| | - Marc Hafner
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
- Genentech, Inc., South San Francisco, CA, 94080, USA
| | - Mario Niepel
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
- Ribon Therapeutics, Inc., Cambridge, MA, 02140, USA
| | - Luca Gerosa
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
- Genentech, Inc., South San Francisco, CA, 94080, USA
| | - Mirra Chung
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Chiara Victor
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Benjamin Gaudio
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Clarence Yapp
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Ajit J Nirmal
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
| | - Nicholas Clark
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA.
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33
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Severin Y, Hale BD, Mena J, Goslings D, Frey BM, Snijder B. Multiplexed high-throughput immune cell imaging reveals molecular health-associated phenotypes. SCIENCE ADVANCES 2022; 8:eabn5631. [PMID: 36322666 PMCID: PMC9629716 DOI: 10.1126/sciadv.abn5631] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Phenotypic plasticity is essential to the immune system, yet the factors that shape it are not fully understood. Here, we comprehensively analyze immune cell phenotypes including morphology across human cohorts by single-round multiplexed immunofluorescence, automated microscopy, and deep learning. Using the uncertainty of convolutional neural networks to cluster the phenotypes of eight distinct immune cell subsets, we find that the resulting maps are influenced by donor age, gender, and blood pressure, revealing distinct polarization and activation-associated phenotypes across immune cell classes. We further associate T cell morphology to transcriptional state based on their joint donor variability and validate an inflammation-associated polarized T cell morphology and an age-associated loss of mitochondria in CD4+ T cells. Together, we show that immune cell phenotypes reflect both molecular and personal health information, opening new perspectives into the deep immune phenotyping of individual people in health and disease.
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Affiliation(s)
- Yannik Severin
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8049 Zürich, Switzerland
| | - Benjamin D. Hale
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8049 Zürich, Switzerland
| | - Julien Mena
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8049 Zürich, Switzerland
| | - David Goslings
- Blood Transfusion Service Zürich, SRC, 8952 Schlieren, Switzerland
| | - Beat M. Frey
- Blood Transfusion Service Zürich, SRC, 8952 Schlieren, Switzerland
| | - Berend Snijder
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8049 Zürich, Switzerland
- Corresponding author.
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34
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Walton RT, Singh A, Blainey PC. Pooled genetic screens with image-based profiling. Mol Syst Biol 2022; 18:e10768. [PMID: 36366905 PMCID: PMC9650298 DOI: 10.15252/msb.202110768] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 09/12/2022] [Accepted: 09/16/2022] [Indexed: 11/13/2022] Open
Abstract
Spatial structure in biology, spanning molecular, organellular, cellular, tissue, and organismal scales, is encoded through a combination of genetic and epigenetic factors in individual cells. Microscopy remains the most direct approach to exploring the intricate spatial complexity defining biological systems and the structured dynamic responses of these systems to perturbations. Genetic screens with deep single-cell profiling via image features or gene expression programs have the capacity to show how biological systems work in detail by cataloging many cellular phenotypes with one experimental assay. Microscopy-based cellular profiling provides information complementary to next-generation sequencing (NGS) profiling and has only recently become compatible with large-scale genetic screens. Optical screening now offers the scale needed for systematic characterization and is poised for further scale-up. We discuss how these methodologies, together with emerging technologies for genetic perturbation and microscopy-based multiplexed molecular phenotyping, are powering new approaches to reveal genotype-phenotype relationships.
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Affiliation(s)
- Russell T Walton
- Broad Institute of MIT and HarvardCambridgeMAUSA
- Department of Biological EngineeringMITCambridgeMAUSA
| | - Avtar Singh
- Broad Institute of MIT and HarvardCambridgeMAUSA
- Present address:
Department of Cellular and Tissue GenomicsGenentechSouth San FranciscoCAUSA
| | - Paul C Blainey
- Broad Institute of MIT and HarvardCambridgeMAUSA
- Department of Biological EngineeringMITCambridgeMAUSA
- Koch Institute for Integrative Cancer ResearchMITCambridgeMAUSA
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35
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Rubert J, Gatto P, Pancher M, Sidarovich V, Curti C, Mena P, Del Rio D, Quattrone A, Mattivi F. A Screening of Native (Poly)phenols and Gut-Related Metabolites on 3D HCT116 Spheroids Reveals Gut Health Benefits of a Flavan-3-ol Metabolite. Mol Nutr Food Res 2022; 66:e2101043. [PMID: 35394679 PMCID: PMC9787721 DOI: 10.1002/mnfr.202101043] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 03/19/2022] [Indexed: 12/30/2022]
Abstract
SCOPE Epidemiological evidence suggests that a reduced risk of colorectal cancer (CRC) is correlated with high consumption of fruits and vegetables, which are major sources of fiber and phytochemicals, such as flavan-3-ols. However, it remains unknown how these phytochemicals and their specific gut-related metabolites may alter cancer cell behavior. METHODS AND RESULTS A focused screening using native (poly)phenols and gut microbial metabolites (GMMs) on 3D HCT116 spheroids is carried out using a high-throughput imaging approach. Dose-responses, IC50 , and long-term exposure are calculated for the most promising native (poly)phenols and GMMs. As a result, this research shows that (poly)phenol catabolites may play a key role in preventing cancer propagation. Indeed, µM concentration levels of (4R)-5-(3',4'-dihydroxyphenyl)-γ-valerolactone significantly decrease spheroid size at early stages of spheroid aggregation and gene expression of matrix metalloproteinases. CONCLUSION A chronic exposure to (4R)-5-(3',4'-dihydroxyphenyl)-γ-valerolactone may lead to a reduced CRC risk. Daily intake of monomeric, oligomeric, and polymeric flavan-3-ols may increase the colonic concentrations of this metabolite, and, in turn, this compound may act locally interacting with intestinal epithelial cells, precancerous and cancer cells.
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Affiliation(s)
- Josep Rubert
- Food Quality and DesignWageningen University & ResearchBornse Weilanden 9Wageningen6708 WGThe Netherlands
- Division of Human Nutrition and HealthWageningen University & ResearchStippeneng 4Wageningen6708 WEThe Netherlands
| | - Pamela Gatto
- HTS and Validation Core FacilityDept. CIBIO ‐ Department of CellularComputational and Integrative BiologyUniversity of TrentoVia Sommarive 9Trento38123Italy
| | - Michael Pancher
- HTS and Validation Core FacilityDept. CIBIO ‐ Department of CellularComputational and Integrative BiologyUniversity of TrentoVia Sommarive 9Trento38123Italy
| | - Viktoryia Sidarovich
- HTS and Validation Core FacilityDept. CIBIO ‐ Department of CellularComputational and Integrative BiologyUniversity of TrentoVia Sommarive 9Trento38123Italy
| | - Claudio Curti
- Department of Food and DrugUniversity of ParmaParco Area delle Scienze, 27/AParma43124Italy
| | - Pedro Mena
- Human Nutrition UnitDepartment of Food and DrugUniversity of ParmaMedical School Building C, Via Volturno, 39Parma43125Italy
- Microbiome Research HubUniversity of ParmaParma43124Italy
| | - Daniele Del Rio
- Human Nutrition UnitDepartment of Food and DrugUniversity of ParmaMedical School Building C, Via Volturno, 39Parma43125Italy
- Microbiome Research HubUniversity of ParmaParma43124Italy
- School of Advanced Studies on Food and NutritionUniversity of ParmaParma43126Italy
| | - Alessandro Quattrone
- Laboratory of Translational GenomicsDept. CIBIO ‐ Department of CellularComputational and Integrative BiologyUniversity of TrentoVia Sommarive 9Trento38123Italy
| | - Fulvio Mattivi
- Dept. CIBIO ‐ Department of CellularComputational and Integrative BiologyUniversity of TrentoVia Sommarive 9Trento38123Italy
- Metabolomics UnitDepartment of Food Quality and NutritionFondazione Edmund Mach ‐ FEMResearch and Innovation CentreVia Mach 1San Michele all'Adige38098Italy
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36
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Comprehensive single-shot biophysical cytometry using simultaneous quantitative phase imaging and Brillouin spectroscopy. Sci Rep 2022; 12:18285. [PMID: 36316372 PMCID: PMC9622723 DOI: 10.1038/s41598-022-23049-4] [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: 07/07/2022] [Accepted: 10/25/2022] [Indexed: 11/06/2022] Open
Abstract
Single-cell analysis, or cytometry, is a ubiquitous tool in the biomedical sciences. Whereas most cytometers use fluorescent probes to ascertain the presence or absence of targeted molecules, biophysical parameters such as the cell density, refractive index, and viscosity are difficult to obtain. In this work, we combine two complementary techniques-quantitative phase imaging and Brillouin spectroscopy-into a label-free image cytometry platform capable of measuring more than a dozen biophysical properties of individual cells simultaneously. Using a geometric simplification linked to freshly plated cells, we can acquire the cellular diameter, volume, refractive index, mass density, non-aqueous mass, fluid volume, dry volume, the fractional water content of cells, both by mass and by volume, the Brillouin shift, Brillouin linewidth, longitudinal modulus, longitudinal viscosity, the loss modulus, and the loss tangent, all from a single acquisition, and with no assumptions of underlying parameters. Our methods are validated across three cell populations, including a control population of CHO-K1 cells, cells exposed to tubulin-disrupting nocodazole, and cells under hypoosmotic shock. Our system will unlock new avenues of research in biophysics, cell biology, and medicine.
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Yang S, Shen T, Fang Y, Wang X, Zhang J, Yang W, Huang J, Han X. DeepNoise: Signal and Noise Disentanglement Based on Classifying Fluorescent Microscopy Images via Deep Learning. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:989-1001. [PMID: 36608842 PMCID: PMC10025761 DOI: 10.1016/j.gpb.2022.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 11/26/2022] [Accepted: 12/11/2022] [Indexed: 01/05/2023]
Abstract
The high-content image-based assay is commonly leveraged for identifying the phenotypic impact of genetic perturbations in biology field. However, a persistent issue remains unsolved during experiments: the interferential technical noises caused by systematic errors (e.g., temperature, reagent concentration, and well location) are always mixed up with the real biological signals, leading to misinterpretation of any conclusion drawn. Here, we reported a mean teacher-based deep learning model (DeepNoise) that can disentangle biological signals from the experimental noises. Specifically, we aimed to classify the phenotypic impact of 1108 different genetic perturbations screened from 125,510 fluorescent microscopy images, which were totally unrecognizable by the human eye. We validated our model by participating in the Recursion Cellular Image Classification Challenge, and DeepNoise achieved an extremely high classification score (accuracy: 99.596%), ranking the 2nd place among 866 participating groups. This promising result indicates the successful separation of biological and technical factors, which might help decrease the cost of treatment development and expedite the drug discovery process. The source code of DeepNoise is available at https://github.com/Scu-sen/Recursion-Cellular-Image-Classification-Challenge.
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Affiliation(s)
- Sen Yang
- Tencent AI Lab, Shenzhen 518057, China
| | - Tao Shen
- Tencent AI Lab, Shenzhen 518057, China
| | - Yuqi Fang
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region 999077, China
| | - Xiyue Wang
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Jun Zhang
- Tencent AI Lab, Shenzhen 518057, China.
| | - Wei Yang
- Tencent AI Lab, Shenzhen 518057, China
| | | | - Xiao Han
- Tencent AI Lab, Shenzhen 518057, China.
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38
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Chand S. Semantic segmentation of human cell nucleus using deep U-Net and other versions of U-Net models. NETWORK (BRISTOL, ENGLAND) 2022; 33:167-186. [PMID: 35822269 DOI: 10.1080/0954898x.2022.2096938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 04/04/2022] [Accepted: 06/27/2022] [Indexed: 06/15/2023]
Abstract
The deep learning models play an essential role in many areas, including medical image analysis. These models extract important features without human intervention. In this paper, we propose a deep convolution neural network, named as deep U-Net model, for the segmentation of the cell nucleus, a critical functional unit that determines the function and structure of the body. The nucleus contains all kinds of DNA, RNA, chromosomes, and genes governing all life activities, and its disorder may lead to different types of diseases such as cancer, heart disease, diabetes, Alzheimer's, etc. If the nucleus structure is known correctly, diseases due to nucleus disorder may be detected early. It may also reduce the drug discovery time if the shape and size of the nucleus are known. We evaluate the performance of the proposed models on the nucleus segmentation data set used by the Data Science Bowl 2018 competition hosted by Kaggle. We compare its performance with that of the U-Net, Attention U-Net, R2U-Net, Attention R2U-Net, and both versions of the U-Net++ with and without supervision, in terms of loss, dice coefficient, dice loss, intersection over union, and accuracy. Our model performs better than the existing models.
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Affiliation(s)
- Satish Chand
- School of Computer and Systems Sciences, Jawaharlal Nehru Univesity, New Delhi, India
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39
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Xu M, Cui Q, Su W, Zhang D, Pan J, Liu X, Pang Z, Zhu Q. High-content screening of active components of Traditional Chinese Medicine inhibiting TGF-β-induced cell EMT. Heliyon 2022; 8:e10238. [PMID: 36042745 PMCID: PMC9420491 DOI: 10.1016/j.heliyon.2022.e10238] [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: 02/25/2022] [Revised: 05/13/2022] [Accepted: 08/05/2022] [Indexed: 11/30/2022] Open
Abstract
The epithelial mesenchymal transition (EMT) has roles in metastasis and invasion during fibrotic diseases and cancer progression. Some Traditional Chinese Medicines (TCMs) have shown inhibitory effects with respect to the EMT. The current study attempted to establish a multiparametric high-content method to screen for active monomeric compounds in TCM with the ability to target cellular EMT by assessing phenotypic changes. A total of 306 monomeric compounds from the MedChemExpress (MCE) compound library were screened by the high-content screening (HCS) system and 5 compounds with anti-EMT activity, including camptothecin (CPT), dimethyl curcumin (DMC), artesunate (ART), sinapine (SNP) and berberine (BER) were identified. To confirm anti-EMT activity, expression of EMT markers was assessed by qRT-PCR and Western blotting, and cell adhesion and migration measured by cell function assays. The results revealed that CPT, DMC, ART, SNP and BER inhibited transforming growth factor-β1 (TGF-β1)-induced expression of vimentin and α-SMA, upregulated expression of E-cadherin, increased cell adhesion and reduced cell migration. In summary, by quantifying the cell morphological changes during TGF-β1-induced EMT through multi-parametric analysis, TCM compounds with anti-EMT activity were successfully screened using the HCS system, a faster and more economical approach than conventional methods.
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Affiliation(s)
- Mengzhen Xu
- College of Pharmaceutical Science, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
| | - Qinghua Cui
- Innovative Institute of Chinese Medicine and Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
| | - Wen Su
- Innovative Institute of Chinese Medicine and Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
| | - Dan Zhang
- Experimental Center, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
| | - Jiaxu Pan
- College of Pharmaceutical Science, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
| | - Xiangqi Liu
- Innovative Institute of Chinese Medicine and Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
| | - Zheng Pang
- Innovative Institute of Chinese Medicine and Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
| | - Qingjun Zhu
- Innovative Institute of Chinese Medicine and Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China.,Key Laboratory of Traditional Chinese Medicine Classical Theory, Ministry of Education, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
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40
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Self-supervised deep learning encodes high-resolution features of protein subcellular localization. Nat Methods 2022; 19:995-1003. [PMID: 35879608 PMCID: PMC9349041 DOI: 10.1038/s41592-022-01541-z] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 05/26/2022] [Indexed: 12/16/2022]
Abstract
Explaining the diversity and complexity of protein localization is essential to fully understand cellular architecture. Here we present cytoself, a deep-learning approach for fully self-supervised protein localization profiling and clustering. Cytoself leverages a self-supervised training scheme that does not require preexisting knowledge, categories or annotations. Training cytoself on images of 1,311 endogenously labeled proteins from the OpenCell database reveals a highly resolved protein localization atlas that recapitulates major scales of cellular organization, from coarse classes, such as nuclear and cytoplasmic, to the subtle localization signatures of individual protein complexes. We quantitatively validate cytoself’s ability to cluster proteins into organelles and protein complexes, showing that cytoself outperforms previous self-supervised approaches. Moreover, to better understand the inner workings of our model, we dissect the emergent features from which our clustering is derived, interpret them in the context of the fluorescence images, and analyze the performance contributions of each component of our approach. Cytoself is a self-supervised deep learning-based approach for profiling and clustering protein localization from fluorescence images. Cytoself outperforms established approaches and can accurately predict protein subcellular localization.
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41
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Anderson CL, Munawar S, Reilly L, Kamp TJ, January CT, Delisle BP, Eckhardt LL. How Functional Genomics Can Keep Pace With VUS Identification. Front Cardiovasc Med 2022; 9:900431. [PMID: 35859585 PMCID: PMC9291992 DOI: 10.3389/fcvm.2022.900431] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 06/09/2022] [Indexed: 01/03/2023] Open
Abstract
Over the last two decades, an exponentially expanding number of genetic variants have been identified associated with inherited cardiac conditions. These tremendous gains also present challenges in deciphering the clinical relevance of unclassified variants or variants of uncertain significance (VUS). This review provides an overview of the advancements (and challenges) in functional and computational approaches to characterize variants and help keep pace with VUS identification related to inherited heart diseases.
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Affiliation(s)
- Corey L. Anderson
- Cellular and Molecular Arrythmias Program, Division of Cardiovascular Medicine, Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Saba Munawar
- Cellular and Molecular Arrythmias Program, Division of Cardiovascular Medicine, Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Louise Reilly
- Cellular and Molecular Arrythmias Program, Division of Cardiovascular Medicine, Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Timothy J. Kamp
- Cellular and Molecular Arrythmias Program, Division of Cardiovascular Medicine, Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Craig T. January
- Cellular and Molecular Arrythmias Program, Division of Cardiovascular Medicine, Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Brian P. Delisle
- Department of Physiology, University of Kentucky College of Medicine, Lexington, KY, United States
| | - Lee L. Eckhardt
- Cellular and Molecular Arrythmias Program, Division of Cardiovascular Medicine, Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States
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42
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Seo Y, Bang S, Son J, Kim D, Jeong Y, Kim P, Yang J, Eom JH, Choi N, Kim HN. Brain physiome: A concept bridging in vitro 3D brain models and in silico models for predicting drug toxicity in the brain. Bioact Mater 2022; 13:135-148. [PMID: 35224297 PMCID: PMC8843968 DOI: 10.1016/j.bioactmat.2021.11.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 11/01/2021] [Accepted: 11/06/2021] [Indexed: 12/12/2022] Open
Abstract
In the last few decades, adverse reactions to pharmaceuticals have been evaluated using 2D in vitro models and animal models. However, with increasing computational power, and as the key drivers of cellular behavior have been identified, in silico models have emerged. These models are time-efficient and cost-effective, but the prediction of adverse reactions to unknown drugs using these models requires relevant experimental input. Accordingly, the physiome concept has emerged to bridge experimental datasets with in silico models. The brain physiome describes the systemic interactions of its components, which are organized into a multilevel hierarchy. Because of the limitations in obtaining experimental data corresponding to each physiome component from 2D in vitro models and animal models, 3D in vitro brain models, including brain organoids and brain-on-a-chip, have been developed. In this review, we present the concept of the brain physiome and its hierarchical organization, including cell- and tissue-level organizations. We also summarize recently developed 3D in vitro brain models and link them with the elements of the brain physiome as a guideline for dataset collection. The connection between in vitro 3D brain models and in silico modeling will lead to the establishment of cost-effective and time-efficient in silico models for the prediction of the safety of unknown drugs.
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Affiliation(s)
- Yoojin Seo
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
| | - Seokyoung Bang
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
| | - Jeongtae Son
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Dongsup Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Yong Jeong
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Pilnam Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Jihun Yang
- Next&Bio Inc., Seoul, 02841, Republic of Korea
| | - Joon-Ho Eom
- Medical Device Research Division, National Institute of Food and Drug Safety Evaluation, Cheongju, 28159, Republic of Korea
| | - Nakwon Choi
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
- Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology (UST), Seoul, 02792, Republic of Korea
- KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, 02841, Republic of Korea
| | - Hong Nam Kim
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
- Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology (UST), Seoul, 02792, Republic of Korea
- School of Mechanical Engineering, Yonsei University, Seoul, 03722, Republic of Korea
- Yonsei-KIST Convergence Research Institute, Yonsei University, Seoul, 03722, Republic of Korea
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43
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Vijayan A, Strauss S, Tofanelli R, Mody TA, Lee K, Tsiantis M, Smith RS, Schneitz K. The annotation and analysis of complex 3D plant organs using 3DCoordX. PLANT PHYSIOLOGY 2022; 189:1278-1295. [PMID: 35348744 PMCID: PMC9237718 DOI: 10.1093/plphys/kiac145] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 03/05/2022] [Indexed: 06/14/2023]
Abstract
A fundamental question in biology concerns how molecular and cellular processes become integrated during morphogenesis. In plants, characterization of 3D digital representations of organs at single-cell resolution represents a promising approach to addressing this problem. A major challenge is to provide organ-centric spatial context to cells of an organ. We developed several general rules for the annotation of cell position and embodied them in 3DCoordX, a user-interactive computer toolbox implemented in the open-source software MorphoGraphX. 3DCoordX enables rapid spatial annotation of cells even in highly curved biological shapes. Using 3DCoordX, we analyzed cellular growth patterns in organs of several species. For example, the data indicated the presence of a basal cell proliferation zone in the ovule primordium of Arabidopsis (Arabidopsis thaliana). Proof-of-concept analyses suggested a preferential increase in cell length associated with neck elongation in the archegonium of Marchantia (Marchantia polymorpha) and variations in cell volume linked to central morphogenetic features of a trap of the carnivorous plant Utricularia (Utricularia gibba). Our work demonstrates the broad applicability of the developed strategies as they provide organ-centric spatial context to cellular features in plant organs of diverse shape complexity.
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Affiliation(s)
| | | | - Rachele Tofanelli
- Plant Developmental Biology, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Tejasvinee Atul Mody
- Plant Developmental Biology, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | | | - Miltos Tsiantis
- Department of Comparative Developmental and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Richard S Smith
- Department of Comparative Developmental and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
- The John Innes Centre, Norwich, UK
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44
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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: 28] [Impact Index Per Article: 14.0] [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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Kumar R. Materiomically Designed Polymeric Vehicles for Nucleic Acids: Quo Vadis? ACS APPLIED BIO MATERIALS 2022; 5:2507-2535. [PMID: 35642794 DOI: 10.1021/acsabm.2c00346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Despite rapid advances in molecular biology, particularly in site-specific genome editing technologies, such as CRISPR/Cas9 and base editing, financial and logistical challenges hinder a broad population from accessing and benefiting from gene therapy. To improve the affordability and scalability of gene therapy, we need to deploy chemically defined, economical, and scalable materials, such as synthetic polymers. For polymers to deliver nucleic acids efficaciously to targeted cells, they must optimally combine design attributes, such as architecture, length, composition, spatial distribution of monomers, basicity, hydrophilic-hydrophobic phase balance, or protonation degree. Designing polymeric vectors for specific nucleic acid payloads is a multivariate optimization problem wherein even minuscule deviations from the optimum are poorly tolerated. To explore the multivariate polymer design space rapidly, efficiently, and fruitfully, we must integrate parallelized polymer synthesis, high-throughput biological screening, and statistical modeling. Although materiomics approaches promise to streamline polymeric vector development, several methodological ambiguities must be resolved. For instance, establishing a flexible polymer ontology that accommodates recent synthetic advances, enforcing uniform polymer characterization and data reporting standards, and implementing multiplexed in vitro and in vivo screening studies require considerable planning, coordination, and effort. This contribution will acquaint readers with the challenges associated with materiomics approaches to polymeric gene delivery and offers guidelines for overcoming these challenges. Here, we summarize recent developments in combinatorial polymer synthesis, high-throughput screening of polymeric vectors, omics-based approaches to polymer design, barcoding schemes for pooled in vitro and in vivo screening, and identify materiomics-inspired research directions that will realize the long-unfulfilled clinical potential of polymeric carriers in gene therapy.
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Affiliation(s)
- Ramya Kumar
- Department of Chemical & Biological Engineering, Colorado School of Mines, 1613 Illinois St, Golden, Colorado 80401, United States
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PLK inhibitors identified by high content phenotypic screening promote maturation of human PSC-derived cardiomyocytes. Biochem Biophys Res Commun 2022; 620:113-120. [DOI: 10.1016/j.bbrc.2022.06.074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 06/22/2022] [Indexed: 11/16/2022]
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Herbig M, Isozaki A, Di Carlo D, Guck J, Nitta N, Damoiseaux R, Kamikawaji S, Suyama E, Shintaku H, Wu AR, Nikaido I, Goda K. Best practices for reporting throughput in biomedical research. Nat Methods 2022; 19:633-634. [PMID: 35508736 DOI: 10.1038/s41592-022-01483-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Maik Herbig
- Department of Chemistry, The University of Tokyo, Tokyo, Japan
| | - Akihiro Isozaki
- Department of Chemistry, The University of Tokyo, Tokyo, Japan
| | - Dino Di Carlo
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.,California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Mechanical Engineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jochen Guck
- Max Planck Institute for the Science of Light, Erlangen, Germany.,Max-Planck-Zentrum für Physik und Medizin, Erlangen, Germany
| | | | - Robert Damoiseaux
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.,California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Molecular and Medicinal Pharmacology, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Eigo Suyama
- Chugai Pharmaceutical Co., Ltd., Tokyo, Japan
| | | | - Angela Ruohao Wu
- Division of Life Science, The Hong Kong University of Science and Technology, Hong Kong SAR, China.,Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Itoshi Nikaido
- RIKEN Center for Biosystems Dynamics Research, Saitama, Japan.,Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan.,Graduate School of Science and Technology, University of Tsukuba, Saitama, Japan
| | - Keisuke Goda
- Department of Chemistry, The University of Tokyo, Tokyo, Japan. .,Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA. .,Institute of Technological Sciences, Wuhan University, Wuhan, China.
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Zhang YY, Yao YD, Cheng QQ, Huang YF, Zhou H. Establishment of a High Content Image Platform to Measure NF-κB Nuclear Translocation in LPS-Induced RAW264.7 Macrophages for Screening Anti-inflammatory Drug Candidates. Curr Drug Metab 2022; 23:394-414. [PMID: 35410593 DOI: 10.2174/1389200223666220411121614] [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: 06/17/2021] [Revised: 01/19/2022] [Accepted: 01/29/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND High content image (HCI), an automatic imaging and analysis system, provides a fast drug screening method by detecting the subcellular distribution of protein in intact cells. OBJECTIVE This study established the first standardized HCI platform for lipopolysaccharide (LPS)-induced RAW264.7 macrophages to screen anti-inflammatory compounds by measuring nuclear factor-κB (NF-κB) nuclear translocation. METHOD The influence of the cell passages, cell density, LPS induction time and concentration, antibody dilution, serum, dimethyl sulfoxide and analysis parameters on NF-κB nuclear translocation and HCI data quality was optimized. The BAY-11-7085, the positive control for inhibiting NF-κB and Western blot assay were separately employed to verify the stability and reliability of the platform. Lastly, the effect of BHA on NO release, iNOS expression, IL-1β, IL-6, and TNF-α mRNA in LPS-induced RAW264.7 cells was detected. RESULTS The optimal conditions for measuring NF-κB translocation in LPS-induced RAW264.7 cells by HCI were established. Cells that do not exceed 22 passages were seeded at a density of 10 k cells/well and pretreated with compounds following 200 ng/mL LPS for 40 min. Parameters including nuclear area of 65 μm2, cell area of 80 μm2, collar of 0.9 μm and sensitivity of 25% were recommended for image segmentation algorithms in the analysis workstation. Benzoylhypaconine from aconite was screened for the first time as an anti-inflammatory candidate by the established HCI platform. The inhibitory effect of benzoylhypaconine on NF-κB translocation was verified by Western blot. Furthermore, benzoylhypaconine reduced the release of NO, inhibited the expression of iNOS, decreased the mRNA levels of IL-1β, IL-6, and TNF-α. CONCLUSION The established HCI platform could be applied to screen anti-inflammatory compounds by measuring the NF-κB nuclear translocation in LPS-induced RAW264.7 cells.
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Affiliation(s)
- Yan-Yu Zhang
- Faculty of Chinese Medicine and State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macao, P.R. China.,Joint Laboratory for Translational Cancer Research of Chinese Medicine of the Ministry of Education of the People's Republic of China, Guangzhou University of Chinese Medicine, Guangzhou 510006, P.R. China
| | - Yun-Da Yao
- Faculty of Chinese Medicine and State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macao, P.R. China.,Joint Laboratory for Translational Cancer Research of Chinese Medicine of the Ministry of Education of the People's Republic of China, Guangzhou University of Chinese Medicine, Guangzhou 510006, P.R. China
| | - Qi-Qing Cheng
- Faculty of Chinese Medicine and State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macao, P.R. China.,Joint Laboratory for Translational Cancer Research of Chinese Medicine of the Ministry of Education of the People's Republic of China, Guangzhou University of Chinese Medicine, Guangzhou 510006, P.R. China
| | - Yu-Feng Huang
- Faculty of Chinese Medicine and State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macao, P.R. China.,Joint Laboratory for Translational Cancer Research of Chinese Medicine of the Ministry of Education of the People's Republic of China, Guangzhou University of Chinese Medicine, Guangzhou 510006, P.R. China
| | - Hua Zhou
- Faculty of Chinese Medicine and State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macao, P.R. China.,Joint Laboratory for Translational Cancer Research of Chinese Medicine of the Ministry of Education of the People's Republic of China, Guangzhou University of Chinese Medicine, Guangzhou 510006, P.R. China.,Zhuhai Hospital of Integrated Traditional Chinese and Western Medicine, Zhuhai City, Guangdong Province 519000, P.R. China
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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: 5] [Impact Index Per Article: 2.5] [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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Trends in pharmaceutical analysis and quality control by modern Raman spectroscopic techniques. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116623] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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