1
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Ghanegolmohammadi F, Eslami M, Ohya Y. Systematic data analysis pipeline for quantitative morphological cell phenotyping. Comput Struct Biotechnol J 2024; 23:2949-2962. [PMID: 39104709 PMCID: PMC11298594 DOI: 10.1016/j.csbj.2024.07.012] [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: 05/06/2024] [Revised: 07/09/2024] [Accepted: 07/10/2024] [Indexed: 08/07/2024] Open
Abstract
Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less.
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Affiliation(s)
- Farzan Ghanegolmohammadi
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
| | - Mohammad Eslami
- Harvard Ophthalmology AI Lab, Schepen’s Eye Research Institute of Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, USA
| | - Yoshikazu Ohya
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
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2
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Borowa A, Rymarczyk D, Żyła M, Kańdula M, Sánchez-Fernández A, Rataj K, Struski Ł, Tabor J, Zieliński B. Decoding phenotypic screening: A comparative analysis of image representations. Comput Struct Biotechnol J 2024; 23:1181-1188. [PMID: 38510976 PMCID: PMC10951426 DOI: 10.1016/j.csbj.2024.02.022] [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: 11/13/2023] [Revised: 02/26/2024] [Accepted: 02/26/2024] [Indexed: 03/22/2024] Open
Abstract
Biomedical imaging techniques such as high content screening (HCS) are valuable for drug discovery, but high costs limit their use to pharmaceutical companies. To address this issue, The JUMP-CP consortium released a massive open image dataset of chemical and genetic perturbations, providing a valuable resource for deep learning research. In this work, we aim to utilize the JUMP-CP dataset to develop a universal representation model for HCS data, mainly data generated using U2OS cells and CellPainting protocol, using supervised and self-supervised learning approaches. We propose an evaluation protocol that assesses their performance on mode of action and property prediction tasks using a popular phenotypic screening dataset. Results show that the self-supervised approach that uses data from multiple consortium partners provides representation that is more robust to batch effects whilst simultaneously achieving performance on par with standard approaches. Together with other conclusions, it provides recommendations on the training strategy of a representation model for HCS images.
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Affiliation(s)
- Adriana Borowa
- Jagiellonian University, Faculty of Mathematics and Computer Science, Kraków, Poland
- Jagiellonian University, Doctoral School of Exact and Natural Sciences, Kraków, Poland
- Ardigen SA, Kraków, Poland
| | - Dawid Rymarczyk
- Jagiellonian University, Faculty of Mathematics and Computer Science, Kraków, Poland
- Ardigen SA, Kraków, Poland
| | | | | | | | | | - Łukasz Struski
- Jagiellonian University, Faculty of Mathematics and Computer Science, Kraków, Poland
| | - Jacek Tabor
- Jagiellonian University, Faculty of Mathematics and Computer Science, Kraków, Poland
| | - Bartosz Zieliński
- Jagiellonian University, Faculty of Mathematics and Computer Science, Kraków, Poland
- Ardigen SA, Kraków, Poland
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3
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Wang Z, Liu Q, Zhou J, Su X. Single-detector multiplex imaging flow cytometry for cancer cell classification with deep learning. Cytometry A 2024. [PMID: 39101554 DOI: 10.1002/cyto.a.24890] [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: 12/21/2023] [Revised: 06/27/2024] [Accepted: 07/22/2024] [Indexed: 08/06/2024]
Abstract
Imaging flow cytometry, which combines the advantages of flow cytometry and microscopy, has emerged as a powerful tool for cell analysis in various biomedical fields such as cancer detection. In this study, we develop multiplex imaging flow cytometry (mIFC) by employing a spatial wavelength division multiplexing technique. Our mIFC can simultaneously obtain brightfield and multi-color fluorescence images of individual cells in flow, which are excited by a metal halide lamp and measured by a single detector. Statistical analysis results of multiplex imaging experiments with resolution test lens, magnification test lens, and fluorescent microspheres validate the operation of the mIFC with good imaging channel consistency and micron-scale differentiation capabilities. A deep learning method is designed for multiplex image processing that consists of three deep learning networks (U-net, very deep super resolution, and visual geometry group 19). It is demonstrated that the cluster of differentiation 24 (CD24) imaging channel is more sensitive than the brightfield, nucleus, or cancer antigen 125 (CA125) imaging channel in classifying the three types of ovarian cell lines (IOSE80 normal cell, A2780, and OVCAR3 cancer cells). An average accuracy rate of 97.1% is achieved for the classification of these three types of cells by deep learning analysis when all four imaging channels are considered. Our single-detector mIFC is promising for the development of future imaging flow cytometers and for the automatic single-cell analysis with deep learning in various biomedical fields.
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Affiliation(s)
- Zhiwen Wang
- School of Integrated Circuits, Shandong University, Jinan, China
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Qiao Liu
- Department of Molecular Medicine and Genetics, School of Basic Medicine Sciences, Shandong University, Jinan, China
| | - Jie Zhou
- School of Integrated Circuits, Shandong University, Jinan, China
| | - Xuantao Su
- School of Integrated Circuits, Shandong University, Jinan, China
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4
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Arevalo J, Su E, Ewald JD, van Dijk R, Carpenter AE, Singh S. Evaluating batch correction methods for image-based cell profiling. Nat Commun 2024; 15:6516. [PMID: 39095341 PMCID: PMC11297288 DOI: 10.1038/s41467-024-50613-5] [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: 08/29/2023] [Accepted: 07/13/2024] [Indexed: 08/04/2024] Open
Abstract
High-throughput image-based profiling platforms are powerful technologies capable of collecting data from billions of cells exposed to thousands of perturbations in a time- and cost-effective manner. Therefore, image-based profiling data has been increasingly used for diverse biological applications, such as predicting drug mechanism of action or gene function. However, batch effects severely limit community-wide efforts to integrate and interpret image-based profiling data collected across different laboratories and equipment. To address this problem, we benchmark ten high-performing single-cell RNA sequencing (scRNA-seq) batch correction techniques, representing diverse approaches, using a newly released Cell Painting dataset, JUMP. We focus on five scenarios with varying complexity, ranging from batches prepared in a single lab over time to batches imaged using different microscopes in multiple labs. We find that Harmony and Seurat RPCA are noteworthy, consistently ranking among the top three methods for all tested scenarios while maintaining computational efficiency. Our proposed framework, benchmark, and metrics can be used to assess new batch correction methods in the future. This work paves the way for improvements that enable the community to make the best use of public Cell Painting data for scientific discovery.
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Affiliation(s)
- John Arevalo
- Imaging Platform, Broad Institute of MIT and Harvard, 02142, Cambridge, MA, USA
| | - Ellen Su
- Imaging Platform, Broad Institute of MIT and Harvard, 02142, Cambridge, MA, USA
| | - Jessica D Ewald
- Imaging Platform, Broad Institute of MIT and Harvard, 02142, Cambridge, MA, USA
| | - Robert van Dijk
- Imaging Platform, Broad Institute of MIT and Harvard, 02142, Cambridge, MA, USA
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard, 02142, Cambridge, MA, USA
| | - Shantanu Singh
- Imaging Platform, Broad Institute of MIT and Harvard, 02142, Cambridge, MA, USA.
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5
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Lo MCK, Siu DMD, Lee KCM, Wong JSJ, Yeung MCF, Hsin MKY, Ho JCM, Tsia KK. Information-Distilled Generative Label-Free Morphological Profiling Encodes Cellular Heterogeneity. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307591. [PMID: 38864546 PMCID: PMC11304271 DOI: 10.1002/advs.202307591] [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: 10/11/2023] [Revised: 05/17/2024] [Indexed: 06/13/2024]
Abstract
Image-based cytometry faces challenges due to technical variations arising from different experimental batches and conditions, such as differences in instrument configurations or image acquisition protocols, impeding genuine biological interpretation of cell morphology. Existing solutions, often necessitating extensive pre-existing data knowledge or control samples across batches, have proved limited, especially with complex cell image data. To overcome this, "Cyto-Morphology Adversarial Distillation" (CytoMAD), a self-supervised multi-task learning strategy that distills biologically relevant cellular morphological information from batch variations, is introduced to enable integrated analysis across multiple data batches without complex data assumptions or extensive manual annotation. Unique to CytoMAD is its "morphology distillation", symbiotically paired with deep-learning image-contrast translation-offering additional interpretable insights into label-free cell morphology. The versatile efficacy of CytoMAD is demonstrated in augmenting the power of biophysical imaging cytometry. It allows integrated label-free classification of human lung cancer cell types and accurately recapitulates their progressive drug responses, even when trained without the drug concentration information. CytoMAD also allows joint analysis of tumor biophysical cellular heterogeneity, linked to epithelial-mesenchymal plasticity, that standard fluorescence markers overlook. CytoMAD can substantiate the wide adoption of biophysical cytometry for cost-effective diagnosis and screening.
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Affiliation(s)
- Michelle C. K. Lo
- Department of Electrical and Electronic EngineeringThe University of Hong KongHong Kong000000Hong Kong
- Advanced Biomedical Instrumentation CentreHong Kong Science Park, New TerritoriesHong Kong000000Hong Kong
| | - Dickson M. D. Siu
- Department of Electrical and Electronic EngineeringThe University of Hong KongHong Kong000000Hong Kong
- Advanced Biomedical Instrumentation CentreHong Kong Science Park, New TerritoriesHong Kong000000Hong Kong
| | - Kelvin C. M. Lee
- Department of Electrical and Electronic EngineeringThe University of Hong KongHong Kong000000Hong Kong
- Advanced Biomedical Instrumentation CentreHong Kong Science Park, New TerritoriesHong Kong000000Hong Kong
| | - Justin S. J. Wong
- Conzeb LimitedHong Kong Science Park, New TerritoriesHong Kong000000Hong Kong
| | - Maximus C. F. Yeung
- Department of Pathology, Li Ka Shing Faculty of MedicineThe University of Hong KongPokfulam RoadHong Kong000000Hong Kong
| | - Michael K. Y. Hsin
- Department of Surgery, Li Ka Shing Faculty of MedicineThe University of Hong KongPokfulam RoadHong Kong000000Hong Kong
| | - James C. M. Ho
- Department of Medicine, Li Ka Shing Faculty of MedicineThe University of Hong KongPokfulam RoadHong Kong000000Hong Kong
| | - Kevin K. Tsia
- Department of Electrical and Electronic EngineeringThe University of Hong KongHong Kong000000Hong Kong
- Advanced Biomedical Instrumentation CentreHong Kong Science Park, New TerritoriesHong Kong000000Hong Kong
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6
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Carreras-Puigvert J, Spjuth O. Artificial intelligence for high content imaging in drug discovery. Curr Opin Struct Biol 2024; 87:102842. [PMID: 38797109 DOI: 10.1016/j.sbi.2024.102842] [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: 02/26/2024] [Revised: 04/28/2024] [Accepted: 04/29/2024] [Indexed: 05/29/2024]
Abstract
Artificial intelligence (AI) and high-content imaging (HCI) are contributing to advancements in drug discovery, propelled by the recent progress in deep neural networks. This review highlights AI's role in analysis of HCI data from fixed and live-cell imaging, enabling novel label-free and multi-channel fluorescent screening methods, and improving compound profiling. HCI experiments are rapid and cost-effective, facilitating large data set accumulation for AI model training. However, the success of AI in drug discovery also depends on high-quality data, reproducible experiments, and robust validation to ensure model performance. Despite challenges like the need for annotated compounds and managing vast image data, AI's potential in phenotypic screening and drug profiling is significant. Future improvements in AI, including increased interpretability and integration of multiple modalities, are expected to solidify AI and HCI's role in drug discovery.
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Affiliation(s)
- Jordi Carreras-Puigvert
- Department of Pharmaceutical Biosciences and Science for Life Laboratories, Uppsala University, Sweden.
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences and Science for Life Laboratories, Uppsala University, Sweden.
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7
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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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8
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Cutrona MB, Wu J, Yang K, Peng J, Chen T. Pancreatic cancer organoid-screening captures personalized sensitivity and chemoresistance suppression upon cytochrome P450 3A5-targeted inhibition. iScience 2024; 27:110289. [PMID: 39055940 PMCID: PMC11269815 DOI: 10.1016/j.isci.2024.110289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 04/12/2024] [Accepted: 06/13/2024] [Indexed: 07/28/2024] Open
Abstract
Cytochrome P450 3A5 (CYP3A5) has been proposed as a predictor of therapy response in subtypes of pancreatic ductal adenocarcinoma cancer (PDAC). To validate CYP3A5 as a therapeutic target, we developed a high-content image organoid-based screen to quantify the phenotypic responses to the selective inhibition of CYP3A5 enzymatic activity by clobetasol propionate (CBZ), using a cohort of PDAC-derived organoids (PDACOs). The chemoresistance of PDACOs to a panel of standard-of-care drugs, alone or in combination with CBZ, was investigated. PDACO pharmaco-profiling revealed CBZ to have anti-cancer activity that was dependent on the CYP3A5 level. In addition, CBZ restored chemo-vulnerability to cisplatin in a subset of PDACOs. A correlative proteomic analysis established that CBZ caused the suppression of multiple cancer pathways sustained by or associated with a mutant form of p53. Limiting the active pool of CYP3A5 enables targeted and personalized therapy to suppress pro-oncogenic mechanisms that fuel chemoresistance in some PDAC tumors.
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Affiliation(s)
- Meritxell B. Cutrona
- Department of Chemical Biology and Therapeutics, St. Jude Children’s Research Hospital, Memphis, TN 38105-3678, USA
| | - Jing Wu
- Department of Chemical Biology and Therapeutics, St. Jude Children’s Research Hospital, Memphis, TN 38105-3678, USA
| | - Ka Yang
- Department of Structural Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105-3678, USA
| | - Junmin Peng
- Department of Structural Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105-3678, USA
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105-3678, USA
| | - Taosheng Chen
- Department of Chemical Biology and Therapeutics, St. Jude Children’s Research Hospital, Memphis, TN 38105-3678, USA
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9
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Odje F, Meijer D, von Coburg E, van der Hooft JJJ, Dunst S, Medema MH, Volkamer A. Unleashing the potential of cell painting assays for compound activities and hazards prediction. FRONTIERS IN TOXICOLOGY 2024; 6:1401036. [PMID: 39086553 PMCID: PMC11288911 DOI: 10.3389/ftox.2024.1401036] [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: 03/14/2024] [Accepted: 06/14/2024] [Indexed: 08/02/2024] Open
Abstract
The cell painting (CP) assay has emerged as a potent imaging-based high-throughput phenotypic profiling (HTPP) tool that provides comprehensive input data for in silico prediction of compound activities and potential hazards in drug discovery and toxicology. CP enables the rapid, multiplexed investigation of various molecular mechanisms for thousands of compounds at the single-cell level. The resulting large volumes of image data provide great opportunities but also pose challenges to image and data analysis routines as well as property prediction models. This review addresses the integration of CP-based phenotypic data together with or in substitute of structural information from compounds into machine (ML) and deep learning (DL) models to predict compound activities for various human-relevant disease endpoints and to identify the underlying modes-of-action (MoA) while avoiding unnecessary animal testing. The successful application of CP in combination with powerful ML/DL models promises further advances in understanding compound responses of cells guiding therapeutic development and risk assessment. Therefore, this review highlights the importance of unlocking the potential of CP assays when combined with molecular fingerprints for compound evaluation and discusses the current challenges that are associated with this approach.
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Affiliation(s)
- Floriane Odje
- Data Driven Drug Design, Center for Bioinformatics, Saarland University, Saarbrücken, Germany
| | - David Meijer
- Bioinformatics Group, Wageningen University, Wageningen, Netherlands
| | - Elena von Coburg
- Department Experimental Toxicology and ZEBET, German Federal Institute for Risk Assessment (BfR), German Centre for the Protection of Laboratory Animals (Bf3R), Berlin, Germany
| | | | - Sebastian Dunst
- Department Experimental Toxicology and ZEBET, German Federal Institute for Risk Assessment (BfR), German Centre for the Protection of Laboratory Animals (Bf3R), Berlin, Germany
| | - Marnix H. Medema
- Bioinformatics Group, Wageningen University, Wageningen, Netherlands
| | - Andrea Volkamer
- Data Driven Drug Design, Center for Bioinformatics, Saarland University, Saarbrücken, Germany
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10
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Grosicki M, Wojnar-Lason K, Mosiolek S, Mateuszuk L, Stojak M, Chlopicki S. Distinct profile of antiviral drugs effects in aortic and pulmonary endothelial cells revealed by high-content microscopy and cell painting assays. Toxicol Appl Pharmacol 2024; 490:117030. [PMID: 38981531 DOI: 10.1016/j.taap.2024.117030] [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: 04/03/2024] [Revised: 06/28/2024] [Accepted: 07/05/2024] [Indexed: 07/11/2024]
Abstract
Antiretroviral therapy have significantly improved the treatment of viral infections and reduced the associated mortality and morbidity rates. However, highly effective antiretroviral therapy (HAART) may lead to an increased risk of cardiovascular diseases, which could be related to endothelial toxicity. Here, seven antiviral drugs (remdesivir, PF-00835231, ritonavir, lopinavir, efavirenz, zidovudine and abacavir) were characterized against aortic (HAEC) and pulmonary (hLMVEC) endothelial cells, using high-content microscopy. The colourimetric study (MTS test) revealed similar toxicity profiles of all antiviral drugs tested in the concentration range of 1 nM-50 μM in aortic and pulmonary endothelial cells. Conversely, the drugs' effects on morphological parameters were more pronounced in HAECs as compared with hLMVECs. Based on the antiviral drugs' effects on the cytoplasmic and nuclei architecture (analyzed by multiple pre-defined parameters including SER texture and STAR morphology), the studied compounds were classified into five distinct morphological subgroups, each linked to a specific cellular response profile. In relation to morphological subgroup classification, antiviral drugs induced a loss of mitochondrial membrane potential, elevated ROS, changed lipid droplets/lysosomal content, decreased von Willebrand factor expression and micronuclei formation or dysregulated cellular autophagy. In conclusion, based on specific changes in endothelial cytoplasm, nuclei and subcellular morphology, the distinct endothelial response was identified for remdesivir, ritonavir, lopinavir, efavirenz, zidovudine and abacavir treatments. The effects detected in aortic endothelial cells were not detected in pulmonary endothelial cells. Taken together, high-content microscopy has proven to be a robust and informative method for endothelial drug profiling that may prove useful in predicting the organ-specific endothelial toxicity of various drugs.
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Affiliation(s)
- Marek Grosicki
- Jagiellonian Centre for Experimental Therapeutics (JCET), Jagiellonian University, Krakow, Poland.
| | - Kamila Wojnar-Lason
- Jagiellonian Centre for Experimental Therapeutics (JCET), Jagiellonian University, Krakow, Poland; Department of Pharmacology, Jagiellonian University Medical College, Krakow, Poland
| | - Sylwester Mosiolek
- Jagiellonian Centre for Experimental Therapeutics (JCET), Jagiellonian University, Krakow, Poland; Jagiellonian University, Doctoral School of Exact and Natural Sciences, Krakow, Poland
| | - Lukasz Mateuszuk
- Jagiellonian Centre for Experimental Therapeutics (JCET), Jagiellonian University, Krakow, Poland
| | - Marta Stojak
- Jagiellonian Centre for Experimental Therapeutics (JCET), Jagiellonian University, Krakow, Poland
| | - Stefan Chlopicki
- Jagiellonian Centre for Experimental Therapeutics (JCET), Jagiellonian University, Krakow, Poland; Department of Pharmacology, Jagiellonian University Medical College, Krakow, Poland.
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11
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Di Mauro F, Arbore G. Spatial Dissection of the Immune Landscape of Solid Tumors to Advance Precision Medicine. Cancer Immunol Res 2024; 12:800-813. [PMID: 38657223 PMCID: PMC11217735 DOI: 10.1158/2326-6066.cir-23-0699] [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: 08/28/2023] [Revised: 01/12/2024] [Accepted: 04/19/2024] [Indexed: 04/26/2024]
Abstract
Chemotherapeutics, radiation, targeted therapeutics, and immunotherapeutics each demonstrate clinical benefits for a small subset of patients with solid malignancies. Immune cells infiltrating the tumor and the surrounding stroma play a critical role in shaping cancer progression and modulating therapy response. They do this by interacting with the other cellular and molecular components of the tumor microenvironment. Spatial multi-omics technologies are rapidly evolving. Currently, such technologies allow high-throughput RNA and protein profiling and retain geographical information about the tumor microenvironment cellular architecture and the functional phenotype of tumor, immune, and stromal cells. An in-depth spatial characterization of the heterogeneous tumor immune landscape can improve not only the prognosis but also the prediction of therapy response, directing cancer patients to more tailored and efficacious treatments. This review highlights recent advancements in spatial transcriptomics and proteomics profiling technologies and the ways these technologies are being applied for the dissection of the immune cell composition in solid malignancies in order to further both basic research in oncology and the implementation of precision treatments in the clinic.
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Affiliation(s)
- Francesco Di Mauro
- Vita-Salute San Raffaele University, Milan, Italy.
- Experimental Immunology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
| | - Giuseppina Arbore
- Vita-Salute San Raffaele University, Milan, Italy.
- Experimental Immunology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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12
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Serrano E, Chandrasekaran SN, Bunten D, Brewer KI, Tomkinson J, Kern R, Bornholdt M, Fleming S, Pei R, Arevalo J, Tsang H, Rubinetti V, Tromans-Coia C, Becker T, Weisbart E, Bunne C, Kalinin AA, Senft R, Taylor SJ, Jamali N, Adeboye A, Abbasi HS, Goodman A, Caicedo JC, Carpenter AE, Cimini BA, Singh S, Way GP. Reproducible image-based profiling with Pycytominer. ARXIV 2024:arXiv:2311.13417v2. [PMID: 38045474 PMCID: PMC10690292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Advances in high-throughput microscopy have enabled the rapid acquisition of large numbers of high-content microscopy images. Whether by deep learning or classical algorithms, image analysis pipelines then produce single-cell features. To process these single-cells for downstream applications, we present Pycytominer, a user-friendly, open-source python package that implements the bioinformatics steps, known as "image-based profiling". We demonstrate Pycytominer's usefulness in a machine learning project to predict nuisance compounds that cause undesirable cell injuries.
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Affiliation(s)
- Erik Serrano
- Department of Biomedical Informatics, University of Colorado School of Medicine
| | | | - Dave Bunten
- Department of Biomedical Informatics, University of Colorado School of Medicine
| | | | - Jenna Tomkinson
- Department of Biomedical Informatics, University of Colorado School of Medicine
| | - Roshan Kern
- Department of Biomedical Informatics, University of Colorado School of Medicine
- Case Western Reserve University
| | | | | | - Ruifan Pei
- Imaging Platform, Broad Institute of MIT and Harvard
| | - John Arevalo
- Imaging Platform, Broad Institute of MIT and Harvard
| | - Hillary Tsang
- Imaging Platform, Broad Institute of MIT and Harvard
| | - Vincent Rubinetti
- Department of Biomedical Informatics, University of Colorado School of Medicine
| | | | - Tim Becker
- Imaging Platform, Broad Institute of MIT and Harvard
| | - Erin Weisbart
- Imaging Platform, Broad Institute of MIT and Harvard
| | | | | | - Rebecca Senft
- Imaging Platform, Broad Institute of MIT and Harvard
| | - Stephen J. Taylor
- Department of Biomedical Informatics, University of Colorado School of Medicine
| | - Nasim Jamali
- Imaging Platform, Broad Institute of MIT and Harvard
| | | | | | - Allen Goodman
- Imaging Platform, Broad Institute of MIT and Harvard
- Genentech gRED
| | - Juan C. Caicedo
- Imaging Platform, Broad Institute of MIT and Harvard
- Morgridge Institute for Research, University of Wisconsin-Madison
| | | | | | | | - Gregory P. Way
- Department of Biomedical Informatics, University of Colorado School of Medicine
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13
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Foylan S, Schniete JK, Kölln LS, Dempster J, Hansen CG, Shaw M, Bushell TJ, McConnell G. Mesoscale standing wave imaging. J Microsc 2024; 295:33-41. [PMID: 37156549 DOI: 10.1111/jmi.13189] [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: 03/06/2023] [Revised: 05/03/2023] [Accepted: 05/04/2023] [Indexed: 05/10/2023]
Abstract
Standing wave (SW) microscopy is a method that uses an interference pattern to excite fluorescence from labelled cellular structures and produces high-resolution images of three-dimensional objects in a two-dimensional dataset. SW microscopy is performed with high-magnification, high-numerical aperture objective lenses, and while this results in high-resolution images, the field of view is very small. Here we report upscaling of this interference imaging method from the microscale to the mesoscale using the Mesolens, which has the unusual combination of a low-magnification and high-numerical aperture. With this method, we produce SW images within a field of view of 4.4 mm × 3.0 mm that can readily accommodate over 16,000 cells in a single dataset. We demonstrate the method using both single-wavelength excitation and the multi-wavelength SW method TartanSW. We show application of the method for imaging of fixed and living cells specimens, with the first application of SW imaging to study cells under flow conditions.
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Affiliation(s)
- Shannan Foylan
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Jana Katharina Schniete
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Lisa Sophie Kölln
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - John Dempster
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Carsten Gram Hansen
- Institute for Regeneration and Repair, Queen's Medical Research Institute, University of Edinburgh Centre for Inflammation Research, Edinburgh, UK
| | - Michael Shaw
- Faculty of Engineering Sciences, Department of Computer Science, University College London, London, UK
- Biometrology Group, National Physical Laboratory, Teddington, UK
| | - Trevor John Bushell
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Gail McConnell
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
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14
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Culley S, Caballero AC, Burden JJ, Uhlmann V. Made to measure: An introduction to quantifying microscopy data in the life sciences. J Microsc 2024; 295:61-82. [PMID: 37269048 DOI: 10.1111/jmi.13208] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 05/25/2023] [Accepted: 05/26/2023] [Indexed: 06/04/2023]
Abstract
Images are at the core of most modern biological experiments and are used as a major source of quantitative information. Numerous algorithms are available to process images and make them more amenable to be measured. Yet the nature of the quantitative output that is useful for a given biological experiment is uniquely dependent upon the question being investigated. Here, we discuss the 3 main types of information that can be extracted from microscopy data: intensity, morphology, and object counts or categorical labels. For each, we describe where they come from, how they can be measured, and what may affect the relevance of these measurements in downstream data analysis. Acknowledging that what makes a measurement 'good' is ultimately down to the biological question being investigated, this review aims at providing readers with a toolkit to challenge how they quantify their own data and be critical of conclusions drawn from quantitative bioimage analysis experiments.
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Affiliation(s)
- Siân Culley
- Randall Centre for Cell and Molecular Biophysics, King's College London, London, UK
| | | | | | - Virginie Uhlmann
- European Bioinformatics Institute (EMBL-EBI), EMBL, Cambridge, UK
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15
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Vitale GA, Geibel C, Minda V, Wang M, Aron AT, Petras D. Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products. Nat Prod Rep 2024; 41:885-904. [PMID: 38351834 PMCID: PMC11186733 DOI: 10.1039/d3np00050h] [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: 10/11/2023] [Indexed: 06/20/2024]
Abstract
Covering: 1995 to 2023Advances in bioanalytical methods, particularly mass spectrometry, have provided valuable molecular insights into the mechanisms of life. Non-targeted metabolomics aims to detect and (relatively) quantify all observable small molecules present in a biological system. By comparing small molecule abundances between different conditions or timepoints in a biological system, researchers can generate new hypotheses and begin to understand causes of observed phenotypes. Functional metabolomics aims to investigate the functional roles of metabolites at the scale of the metabolome. However, most functional metabolomics studies rely on indirect measurements and correlation analyses, which leads to ambiguity in the precise definition of functional metabolomics. In contrast, the field of natural products has a history of identifying the structures and bioactivities of primary and specialized metabolites. Here, we propose to expand and reframe functional metabolomics by integrating concepts from the fields of natural products and chemical biology. We highlight emerging functional metabolomics approaches that shift the focus from correlation to physical interactions, and we discuss how this allows researchers to uncover causal relationships between molecules and phenotypes.
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Affiliation(s)
- Giovanni Andrea Vitale
- CMFI Cluster of Excellence, Interfaculty Institute of Microbiology and Medicine, University of Tuebingen, Tuebingen, Germany
| | - Christian Geibel
- CMFI Cluster of Excellence, Interfaculty Institute of Microbiology and Medicine, University of Tuebingen, Tuebingen, Germany
| | - Vidit Minda
- Division of Pharmacology and Pharmaceutical Sciences, University of Missouri - Kansas City, Kansas City, USA
- Department of Chemistry and Biochemistry, University of Denver, Denver, USA.
| | - Mingxun Wang
- Department of Computer Science, University of California Riverside, Riverside, USA.
| | - Allegra T Aron
- Department of Chemistry and Biochemistry, University of Denver, Denver, USA.
| | - Daniel Petras
- CMFI Cluster of Excellence, Interfaculty Institute of Microbiology and Medicine, University of Tuebingen, Tuebingen, Germany
- Department of Biochemistry, University of California Riverside, Riverside, USA.
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16
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Pregowska A, Roszkiewicz A, Osial M, Giersig M. How scanning probe microscopy can be supported by artificial intelligence and quantum computing? Microsc Res Tech 2024. [PMID: 38864463 DOI: 10.1002/jemt.24629] [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: 03/12/2024] [Revised: 05/28/2024] [Accepted: 05/29/2024] [Indexed: 06/13/2024]
Abstract
The impact of Artificial Intelligence (AI) is rapidly expanding, revolutionizing both science and society. It is applied to practically all areas of life, science, and technology, including materials science, which continuously requires novel tools for effective materials characterization. One of the widely used techniques is scanning probe microscopy (SPM). SPM has fundamentally changed materials engineering, biology, and chemistry by providing tools for atomic-precision surface mapping. Despite its many advantages, it also has some drawbacks, such as long scanning times or the possibility of damaging soft-surface materials. In this paper, we focus on the potential for supporting SPM-based measurements, with an emphasis on the application of AI-based algorithms, especially Machine Learning-based algorithms, as well as quantum computing (QC). It has been found that AI can be helpful in automating experimental processes in routine operations, algorithmically searching for optimal sample regions, and elucidating structure-property relationships. Thus, it contributes to increasing the efficiency and accuracy of optical nanoscopy scanning probes. Moreover, the combination of AI-based algorithms and QC may have enormous potential to enhance the practical application of SPM. The limitations of the AI-QC-based approach were also discussed. Finally, we outline a research path for improving AI-QC-powered SPM. RESEARCH HIGHLIGHTS: Artificial intelligence and quantum computing as support for scanning probe microscopy. The analysis indicates a research gap in the field of scanning probe microscopy. The research aims to shed light into ai-qc-powered scanning probe microscopy.
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Affiliation(s)
- Agnieszka Pregowska
- Department of Information and Computational Science, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Agata Roszkiewicz
- Department of Information and Computational Science, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Magdalena Osial
- Department of Information and Computational Science, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Michael Giersig
- Department of Information and Computational Science, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
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17
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Shpigler A, Kolet N, Golan S, Weisbart E, Zaritsky A. Anomaly detection for high-content image-based phenotypic cell profiling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.01.595856. [PMID: 38895267 PMCID: PMC11185510 DOI: 10.1101/2024.06.01.595856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
High-content image-based phenotypic profiling combines automated microscopy and analysis to identify phenotypic alterations in cell morphology and provide insight into the cell's physiological state. Classical representations of the phenotypic profile can not capture the full underlying complexity in cell organization, while recent weakly machine-learning based representation-learning methods are hard to biologically interpret. We used the abundance of control wells to learn the in-distribution of control experiments and use it to formulate a self-supervised reconstruction anomaly-based representation that encodes the intricate morphological inter-feature dependencies while preserving the representation interpretability. The performance of our anomaly-based representations was evaluated for downstream tasks with respect to two classical representations across four public Cell Painting datasets. Anomaly-based representations improved reproducibility, Mechanism of Action classification, and complemented classical representations. Unsupervised explainability of autoencoder-based anomalies identified specific inter-feature dependencies causing anomalies. The general concept of anomaly-based representations can be adapted to other applications in cell biology.
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Affiliation(s)
- Alon Shpigler
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Naor Kolet
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Shahar Golan
- Department of Computer Science, Jerusalem College of Technology, 91160 Jerusalem, Israel
| | - Erin Weisbart
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge (MA), USA
| | - Assaf Zaritsky
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
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18
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Chandrasekaran SN, Cimini BA, Goodale A, Miller L, Kost-Alimova M, Jamali N, Doench JG, Fritchman B, Skepner A, Melanson M, Kalinin AA, Arevalo J, Haghighi M, Caicedo JC, Kuhn D, Hernandez D, Berstler J, Shafqat-Abbasi H, Root DE, Swalley SE, Garg S, Singh S, Carpenter AE. Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations. Nat Methods 2024; 21:1114-1121. [PMID: 38594452 PMCID: PMC11166567 DOI: 10.1038/s41592-024-02241-6] [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: 05/23/2023] [Accepted: 03/11/2024] [Indexed: 04/11/2024]
Abstract
The identification of genetic and chemical perturbations with similar impacts on cell morphology can elucidate compounds' mechanisms of action or novel regulators of genetic pathways. Research on methods for identifying such similarities has lagged due to a lack of carefully designed and well-annotated image sets of cells treated with chemical and genetic perturbations. Here we create such a Resource dataset, CPJUMP1, in which each perturbed gene's product is a known target of at least two chemical compounds in the dataset. We systematically explore the directionality of correlations among perturbations that target the same protein encoded by a given gene, and we find that identifying matches between chemical and genetic perturbations is a challenging task. Our dataset and baseline analyses provide a benchmark for evaluating methods that measure perturbation similarities and impact, and more generally, learn effective representations of cellular state from microscopy images. Such advancements would accelerate the applications of image-based profiling of cellular states, such as uncovering drug mode of action or probing functional genomics.
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Affiliation(s)
| | - Beth A Cimini
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amy Goodale
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Lisa Miller
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Nasim Jamali
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - John G Doench
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Adam Skepner
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - John Arevalo
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | | | | | | | | | - David E Root
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
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19
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Kinget L, Naulaerts S, Govaerts J, Vanmeerbeek I, Sprooten J, Laureano RS, Dubroja N, Shankar G, Bosisio FM, Roussel E, Verbiest A, Finotello F, Ausserhofer M, Lambrechts D, Boeckx B, Wozniak A, Boon L, Kerkhofs J, Zucman-Rossi J, Albersen M, Baldewijns M, Beuselinck B, Garg AD. A spatial architecture-embedding HLA signature to predict clinical response to immunotherapy in renal cell carcinoma. Nat Med 2024; 30:1667-1679. [PMID: 38773341 DOI: 10.1038/s41591-024-02978-9] [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: 09/01/2023] [Accepted: 04/05/2024] [Indexed: 05/23/2024]
Abstract
An important challenge in the real-world management of patients with advanced clear-cell renal cell carcinoma (aRCC) is determining who might benefit from immune checkpoint blockade (ICB). Here we performed a comprehensive multiomics mapping of aRCC in the context of ICB treatment, involving discovery analyses in a real-world data cohort followed by validation in independent cohorts. We cross-connected bulk-tumor transcriptomes across >1,000 patients with validations at single-cell and spatial resolutions, revealing a patient-specific crosstalk between proinflammatory tumor-associated macrophages and (pre-)exhausted CD8+ T cells that was distinguished by a human leukocyte antigen repertoire with higher preference for tumoral neoantigens. A cross-omics machine learning pipeline helped derive a new tumor transcriptomic footprint of neoantigen-favoring human leukocyte antigen alleles. This machine learning signature correlated with positive outcome following ICB treatment in both real-world data and independent clinical cohorts. In experiments using the RENCA-tumor mouse model, CD40 agonism combined with PD1 blockade potentiated both proinflammatory tumor-associated macrophages and CD8+ T cells, thereby achieving maximal antitumor efficacy relative to other tested regimens. Thus, we present a new multiomics and spatial map of the immune-community architecture that drives ICB response in patients with aRCC.
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Affiliation(s)
- Lisa Kinget
- Laboratory of Experimental Oncology, KU Leuven, Leuven, Belgium
- Department of General Medical Oncology, University Hospitals Leuven, Leuven Cancer Institute, Leuven, Belgium
| | - Stefan Naulaerts
- Laboratory of Cell Stress and Immunity (CSI), Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium
| | - Jannes Govaerts
- Laboratory of Cell Stress and Immunity (CSI), Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium
| | - Isaure Vanmeerbeek
- Laboratory of Cell Stress and Immunity (CSI), Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium
| | - Jenny Sprooten
- Laboratory of Cell Stress and Immunity (CSI), Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium
| | - Raquel S Laureano
- Laboratory of Cell Stress and Immunity (CSI), Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium
| | - Nikolina Dubroja
- Translational Cell and Tissue Research, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Gautam Shankar
- Translational Cell and Tissue Research, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Francesca M Bosisio
- Translational Cell and Tissue Research, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Eduard Roussel
- Department of Urology, University Hospitals Leuven, Leuven, Belgium
| | | | - Francesca Finotello
- Department of Molecular Biology, Digital Science Center (DiSC), University of Innsbruck, Innsbruck, Austria
| | - Markus Ausserhofer
- Department of Molecular Biology, Digital Science Center (DiSC), University of Innsbruck, Innsbruck, Austria
| | - Diether Lambrechts
- Laboratory of Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Center for Cancer Biology, VIB, Leuven, Belgium
| | - Bram Boeckx
- Laboratory of Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Center for Cancer Biology, VIB, Leuven, Belgium
| | | | | | - Johan Kerkhofs
- Histocompatibility and Immunogenetics Laboratory, Belgian Red Cross-Flanders, Mechelen, Belgium
| | - Jessica Zucman-Rossi
- Inserm, UMRS-1138, Génomique fonctionnelle des tumeurs solides, Centre de recherche des Cordeliers, Paris, France
| | - Maarten Albersen
- Department of Urology, University Hospitals Leuven, Leuven, Belgium
| | | | - Benoit Beuselinck
- Laboratory of Experimental Oncology, KU Leuven, Leuven, Belgium.
- Department of General Medical Oncology, University Hospitals Leuven, Leuven Cancer Institute, Leuven, Belgium.
| | - Abhishek D Garg
- Laboratory of Cell Stress and Immunity (CSI), Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium.
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20
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Unni R, Zhou M, Wiecha PR, Zheng Y. Advancing materials science through next-generation machine learning. CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE 2024; 30:101157. [PMID: 39077430 PMCID: PMC11285097 DOI: 10.1016/j.cossms.2024.101157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/31/2024]
Abstract
For over a decade, machine learning (ML) models have been making strides in computer vision and natural language processing (NLP), demonstrating high proficiency in specialized tasks. The emergence of large-scale language and generative image models, such as ChatGPT and Stable Diffusion, has significantly broadened the accessibility and application scope of these technologies. Traditional predictive models are typically constrained to mapping input data to numerical values or predefined categories, limiting their usefulness beyond their designated tasks. In contrast, contemporary models employ representation learning and generative modeling, enabling them to extract and encode key insights from a wide variety of data sources and decode them to create novel responses for desired goals. They can interpret queries phrased in natural language to deduce the intended output. In parallel, the application of ML techniques in materials science has advanced considerably, particularly in areas like inverse design, material prediction, and atomic modeling. Despite these advancements, the current models are overly specialized, hindering their potential to supplant established industrial processes. Materials science, therefore, necessitates the creation of a comprehensive, versatile model capable of interpreting human-readable inputs, intuiting a wide range of possible search directions, and delivering precise solutions. To realize such a model, the field must adopt cutting-edge representation, generative, and foundation model techniques tailored to materials science. A pivotal component in this endeavor is the establishment of an extensive, centralized dataset encompassing a broad spectrum of research topics. This dataset could be assembled by crowdsourcing global research contributions and developing models to extract data from existing literature and represent them in a homogenous format. A massive dataset can be used to train a central model that learns the underlying physics of the target areas, which can then be connected to a variety of specialized downstream tasks. Ultimately, the envisioned model would empower users to intuitively pose queries for a wide array of desired outcomes. It would facilitate the search for existing data that closely matches the sought-after solutions and leverage its understanding of physics and material-behavior relationships to innovate new solutions when pre-existing ones fall short.
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Affiliation(s)
- Rohit Unni
- Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
- Texas Materials Institute, The University of Texas at Austin, Austin, TX 78712, USA
| | - Mingyuan Zhou
- Department of Statistics and Data Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- McCombs School of Business, The University of Texas at Austin, Austin, TX 78712, USA
| | | | - Yuebing Zheng
- Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
- Texas Materials Institute, The University of Texas at Austin, Austin, TX 78712, USA
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21
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Tang Q, Ratnayake R, Seabra G, Jiang Z, Fang R, Cui L, Ding Y, Kahveci T, Bian J, Li C, Luesch H, Li Y. Morphological profiling for drug discovery in the era of deep learning. Brief Bioinform 2024; 25:bbae284. [PMID: 38886164 PMCID: PMC11182685 DOI: 10.1093/bib/bbae284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Revised: 05/13/2024] [Accepted: 06/03/2024] [Indexed: 06/20/2024] Open
Abstract
Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high throughput. These efforts have facilitated understanding of compound mechanism of action, drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.
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Affiliation(s)
- Qiaosi Tang
- Calico Life Sciences, South San Francisco, CA 94080, United States
| | - Ranjala Ratnayake
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Gustavo Seabra
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Zhe Jiang
- Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL 32611, United States
| | - Ruogu Fang
- Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL 32611, United States
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL 32611, United States
| | - Lina Cui
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Yousong Ding
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Tamer Kahveci
- Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL 32611, United States
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32611, United States
| | - Chenglong Li
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Hendrik Luesch
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Yanjun Li
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
- Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL 32611, United States
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22
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Naknaen A, Samernate T, Saeju P, Nonejuie P, Chaikeeratisak V. Nucleus-forming jumbophage PhiKZ therapeutically outcompetes non-nucleus-forming jumbophage Callisto. iScience 2024; 27:109790. [PMID: 38726363 PMCID: PMC11079468 DOI: 10.1016/j.isci.2024.109790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 03/21/2024] [Accepted: 04/16/2024] [Indexed: 05/12/2024] Open
Abstract
With the recent resurgence of phage therapy in modern medicine, jumbophages are currently under the spotlight due to their numerous advantages as anti-infective agents. However, most significant discoveries to date have primarily focused on nucleus-forming jumbophages, not their non-nucleus-forming counterparts. In this study, we compare the biological characteristics exhibited by two genetically diverse jumbophages: 1) the well-studied nucleus-forming jumbophage, PhiKZ; and 2) the newly discovered non-nucleus-forming jumbophage, Callisto. Single-cell infection studies further show that Callisto possesses different replication machinery, resulting in a delay in phage maturation compared to that of PhiKZ. The therapeutic potency of both phages was examined in vitro and in vivo, demonstrating that PhiKZ holds certain superior characteristics over Callisto. This research sheds light on the importance of the subcellular infection machinery and the organized progeny maturation process, which could potentially provide valuable insight in the future development of jumbophage-based therapeutics.
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Affiliation(s)
- Ampapan Naknaen
- Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
| | - Thanadon Samernate
- Institute of Molecular Biosciences, Mahidol University, Nakhon Pathom, Thailand
| | - Panida Saeju
- Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
| | - Poochit Nonejuie
- Institute of Molecular Biosciences, Mahidol University, Nakhon Pathom, Thailand
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23
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Wong CH, Wingett SW, Qian C, Hunter MR, Taliaferro JM, Ross-Thriepland D, Bullock SL. Genome-scale requirements for dynein-based transport revealed by a high-content arrayed CRISPR screen. J Cell Biol 2024; 223:e202306048. [PMID: 38448164 PMCID: PMC10916854 DOI: 10.1083/jcb.202306048] [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: 06/10/2023] [Revised: 01/10/2024] [Accepted: 02/19/2024] [Indexed: 03/08/2024] Open
Abstract
The microtubule motor dynein plays a key role in cellular organization. However, little is known about how dynein's biosynthesis, assembly, and functional diversity are orchestrated. To address this issue, we have conducted an arrayed CRISPR loss-of-function screen in human cells using the distribution of dynein-tethered peroxisomes and early endosomes as readouts. From a genome-wide gRNA library, 195 validated hits were recovered and parsed into those impacting multiple dynein cargoes and those whose effects are restricted to a subset of cargoes. Clustering of high-dimensional phenotypic fingerprints revealed co-functional proteins involved in many cellular processes, including several candidate novel regulators of core dynein functions. Further analysis of one of these factors, the RNA-binding protein SUGP1, indicates that it promotes cargo trafficking by sustaining functional expression of the dynein activator LIS1. Our data represent a rich source of new hypotheses for investigating microtubule-based transport, as well as several other aspects of cellular organization captured by our high-content imaging.
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Affiliation(s)
- Chun Hao Wong
- Cell Biology Division, Medical Research Council Laboratory of Molecular Biology, Cambridge, UK
- Centre for Genomic Research, Discovery Sciences, AstraZeneca, Cambridge, UK
| | - Steven W. Wingett
- Cell Biology Division, Medical Research Council Laboratory of Molecular Biology, Cambridge, UK
| | - Chen Qian
- Quantitative Biology, Discovery Sciences, AstraZeneca, Cambridge, UK
| | - Morag Rose Hunter
- Centre for Genomic Research, Discovery Sciences, AstraZeneca, Cambridge, UK
| | - J. Matthew Taliaferro
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Simon L. Bullock
- Cell Biology Division, Medical Research Council Laboratory of Molecular Biology, Cambridge, UK
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24
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Tapaswi A, Cemalovic N, Polemi KM, Sexton JZ, Colacino JA. Applying Cell Painting in Non-Tumorigenic Breast Cells to Understand Impacts of Common Chemical Exposures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.30.591893. [PMID: 38746407 PMCID: PMC11092634 DOI: 10.1101/2024.04.30.591893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
There are a substantial number of chemicals to which individuals in the general population are exposed which have putative, but still poorly understood, links to breast cancer. Cell Painting is a high-content imaging-based in vitro assay that allows for rapid and unbiased measurements of the concentration-dependent effects of chemical exposures on cellular morphology. We optimized the Cell Painting assay and measured the effect of exposure to 16 human exposure relevant chemicals, along with 21 small molecules with known mechanisms of action, for 48 hours in non-tumorigenic mammary epithelial cells, the MCF10A cell line. Through unbiased imaging analyses using CellProfiler, we quantified 3042 morphological features across approximately 1.2 million cells. We used benchmark concentration modeling to quantify significance and dose-dependent directionality to identify morphological features conserved across chemicals and find features that differentiate the effects of toxicants from one another. Benchmark concentrations were compared to chemical exposure biomarker concentration measurements from the National Health and Nutrition Examination Survey to assess which chemicals induce morphological alterations at human-relevant concentrations. Morphometric fingerprint analysis revealed similar phenotypes between small molecules and prioritized NHANES-toxicants guiding further investigation. A comparison of feature fingerprints via hypergeometric analysis revealed significant feature overlaps between chemicals when stratified by compartment and stain. One such example was the similarities between a metabolite of the organochlorine pesticide DDT (p,p'-DDE) and an activator of canonical Wnt signaling CHIR99201. As CHIR99201 is a known Wnt pathway activator and its role in β-catenin translocation is well studied, we studied the translocation of β-catenin following p'-p' DDE exposure in an orthogonal high-content imaging assay. Consistent with activation of Wnt signaling, low dose p',p'-DDE (25nM) significantly enhances the nuclear translocation of β-catenin. Overall, these findings highlight the ability of Cell Painting to enhance mode-of-action studies for toxicants which are common exposures in our environment but have previously been incompletely characterized with respect to breast cancer risk.
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Affiliation(s)
- Anagha Tapaswi
- Department of Environmental Health Sciences, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas Cemalovic
- Department of Environmental Health Sciences, University of Michigan, Ann Arbor, MI, USA
| | - Katelyn M Polemi
- Department of Environmental Health Sciences, University of Michigan, Ann Arbor, MI, USA
| | - Jonathan Z Sexton
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Medicinal Chemistry, University of Michigan School of Pharmacy, Ann Arbor, MI, USA
| | - Justin A Colacino
- Department of Environmental Health Sciences, University of Michigan, Ann Arbor, MI, USA
- Department of Nutritional Sciences, University of Michigan, Ann Arbor, MI, USA
- Program in the Environment, University of Michigan, Ann Arbor MI, USA
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25
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Mukhopadhyay R, Chandel P, Prasad K, Chakraborty U. Machine learning aided single cell image analysis improves understanding of morphometric heterogeneity of human mesenchymal stem cells. Methods 2024; 225:62-73. [PMID: 38490594 DOI: 10.1016/j.ymeth.2024.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 03/10/2024] [Accepted: 03/12/2024] [Indexed: 03/17/2024] Open
Abstract
The multipotent stem cells of our body have been largely harnessed in biotherapeutics. However, as they are derived from multiple anatomical sources, from different tissues, human mesenchymal stem cells (hMSCs) are a heterogeneous population showing ambiguity in their in vitro behavior. Intra-clonal population heterogeneity has also been identified and pre-clinical mechanistic studies suggest that these cumulatively depreciate the therapeutic effects of hMSC transplantation. Although various biomarkers identify these specific stem cell populations, recent artificial intelligence-based methods have capitalized on the cellular morphologies of hMSCs, opening a new approach to understand their attributes. A robust and rapid platform is required to accommodate and eliminate the heterogeneity observed in the cell population, to standardize the quality of hMSC therapeutics globally. Here, we report our primary findings of morphological heterogeneity observed within and across two sources of hMSCs namely, stem cells from human exfoliated deciduous teeth (SHEDs) and human Wharton jelly mesenchymal stem cells (hWJ MSCs), using real-time single-cell images generated on immunophenotyping by imaging flow cytometry (IFC). We used the ImageJ software for identification and comparison between the two types of hMSCs using statistically significant morphometric descriptors that are biologically relevant. To expand on these insights, we have further applied deep learning methods and successfully report the development of a Convolutional Neural Network-based image classifier. In our research, we introduced a machine learning methodology to streamline the entire procedure, utilizing convolutional neural networks and transfer learning for binary classification, achieving an accuracy rate of 97.54%. We have also critically discussed the challenges, comparisons between solutions and future directions of machine learning in hMSC classification in biotherapeutics.
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Affiliation(s)
- Risani Mukhopadhyay
- Manipal Institute of Regenerative Medicine, Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Pulkit Chandel
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Keerthana Prasad
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Uttara Chakraborty
- Manipal Institute of Regenerative Medicine, Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, India.
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26
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Huang K, Li M, Li Q, Chen Z, Zhang Y, Gu Z. Image-based profiling and deep learning reveal morphological heterogeneity of colorectal cancer organoids. Comput Biol Med 2024; 173:108322. [PMID: 38554658 DOI: 10.1016/j.compbiomed.2024.108322] [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: 11/27/2023] [Revised: 02/26/2024] [Accepted: 03/13/2024] [Indexed: 04/02/2024]
Abstract
Patient-derived organoids have proven to be a highly relevant model for evaluating of disease mechanisms and drug efficacies, as they closely recapitulate in vivo physiology. Colorectal cancer organoids, specifically, exhibit a diverse range of morphologies, which have been analyzed with image-based profiling. However, the relationship between morphological subtypes and functional parameters of the organoids remains underexplored. Here, we identified two distinct morphological subtypes ("cystic" and "solid") across 31360 bright field images using image-based profiling, which correlated differently with viability and apoptosis level of colorectal cancer organoids. Leveraging object detection neural networks, we were able to categorize single organoids achieving higher viability scores as "cystic" than "solid" subtype. Furthermore, a deep generative model was proposed to predict apoptosis intensity based on a apoptosis-featured dataset encompassing over 17000 bright field and matched fluorescent images. Notably, a significant correlation of 0.91 between the predicted value and ground truth was achived, underscoring the feasibility of this generative model as a potential means for assessing organoid functional parameters. The underlying cellular heterogeneity of the organoids, i.e., conserved colonic cell types and rare immune components, was also verified with scRNA sequencing, implying a compromised tumor microenvironment. Additionally, the "cystic" subtype was identified as a relapse phenotype featuring intestinal stem cell signatures, suggesting that this visually discernible relapse phenotype shows potential as a novel biomarker for colorectal cancer diagnosis and prognosis. In summary, our findings demonstrate that the morphological heterogeneity of colorectal cancer organoids explicitly recapitulate the association of phenotypic features and exogenous perturbations through the image-based profiling, providing new insights into disease mechanisms.
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Affiliation(s)
- Kai Huang
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Mingyue Li
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Qiwei Li
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China; Institute of Biomaterials and Medical Devices, Southeast University, Suzhou, Jiangsu, 215163, China
| | - Zaozao Chen
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China; Institute of Biomaterials and Medical Devices, Southeast University, Suzhou, Jiangsu, 215163, China.
| | - Ying Zhang
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China.
| | - Zhongze Gu
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China.
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27
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Zhong X, Chen J, Zhang Z, Zhu Q, Ji D, Ke W, Niu C, Wang C, Zhao N, Chen W, Jia K, Liu Q, Song M, Liu C, Wei Y. Development of an Automated Morphometric Approach to Assess Vascular Outcomes following Exposure to Environmental Chemicals in Zebrafish. ENVIRONMENTAL HEALTH PERSPECTIVES 2024; 132:57001. [PMID: 38701112 PMCID: PMC11068156 DOI: 10.1289/ehp13214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 01/17/2024] [Accepted: 03/18/2024] [Indexed: 05/05/2024]
Abstract
BACKGROUND Disruptions in vascular formation attributable to chemical insults is a pivotal risk factor or potential etiology of developmental defects and various disease settings. Among the thousands of chemicals threatening human health, the highly concerning groups prevalent in the environment and detected in biological monitoring in the general population ought to be prioritized because of their high exposure risks. However, the impacts of a large number of environmental chemicals on vasculature are far from understood. The angioarchitecture complexity and technical limitations make it challenging to analyze the entire vasculature efficiently and identify subtle changes through a high-throughput in vivo assay. OBJECTIVES We aimed to develop an automated morphometric approach for the vascular profile and assess the vascular morphology of health-concerning environmental chemicals. METHODS High-resolution images of the entire vasculature in Tg(fli1a:eGFP) zebrafish were collected using a high-content imaging platform. We established a deep learning-based quantitative framework, ECA-ResXUnet, combined with MATLAB to segment the vascular networks and extract features. Vessel scores based on the rates of morphological changes were calculated to rank vascular toxicity. Potential biomarkers were identified by vessel-endothelium-gene-disease integrative analysis. RESULTS Whole-trunk blood vessels and the cerebral vasculature in larvae exposed to 150 representative chemicals were automatically segmented as comparable to human-level accuracy, with sensitivity and specificity of 95.56% and 95.81%, respectively. Chemical treatments led to heterogeneous vascular patterns manifested by 31 architecture indexes, and the common cardinal vein (CCV) was the most affected vessel. The antipsychotic medicine haloperidol, flame retardant 2,2-bis(chloromethyl)trimethylenebis[bis(2-chloroethyl) phosphate], and tert-butylphenyl diphenyl phosphate ranked as the top three in vessel scores. Pesticides accounted for the largest group, with a vessel score of ≥ 1 , characterized by a remarkable inhibition of subintestinal venous plexus and delayed development of CCV. Multiple-concentration evaluation of nine per- and polyfluoroalkyl substances (PFAS) indicated a low-concentration effect on vascular impairment and a positive association between carbon chain length and benchmark concentration. Target vessel-directed single-cell RNA sequencing of f l i 1 a + cells from larvae treated with λ -cyhalothrin , perfluorohexanesulfonic acid, or benzylbutyl phthalate, along with vessel-endothelium-gene-disease integrative analysis, uncovered potential associations with vascular disorders and identified biomarker candidates. DISCUSSION This study provides a novel paradigm for phenotype-driven screenings of vascular-disrupting chemicals by converging morphological and transcriptomic profiles at a high-resolution level, serving as a powerful tool for large-scale toxicity tests. Our approach and the high-quality morphometric data facilitate the precise evaluation of vascular effects caused by environmental chemicals. https://doi.org/10.1289/EHP13214.
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Affiliation(s)
- Xiali Zhong
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Toxicology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Junzhou Chen
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Zhuyi Zhang
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Toxicology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Qicheng Zhu
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Toxicology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Di Ji
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Toxicology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Weijian Ke
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Toxicology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Congying Niu
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Toxicology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Can Wang
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Toxicology, School of Public Health, Sun Yat-sen University, Guangzhou, China
- Department of Chemical and Environmental Engineering, University of California, Riverside, Riverside, California, USA
| | - Nan Zhao
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Wenquan Chen
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Kunkun Jia
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Qian Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
| | - Maoyong Song
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
| | - Chunqiao Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yanhong Wei
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Toxicology, School of Public Health, Sun Yat-sen University, Guangzhou, China
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28
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Tong L, Corrigan A, Kumar NR, Hallbrook K, Orme J, Wang Y, Zhou H. CLANet: A comprehensive framework for cross-batch cell line identification using brightfield images. Med Image Anal 2024; 94:103123. [PMID: 38430651 DOI: 10.1016/j.media.2024.103123] [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: 06/26/2023] [Revised: 02/23/2024] [Accepted: 02/25/2024] [Indexed: 03/05/2024]
Abstract
Cell line authentication plays a crucial role in the biomedical field, ensuring researchers work with accurately identified cells. Supervised deep learning has made remarkable strides in cell line identification by studying cell morphological features through cell imaging. However, biological batch (bio-batch) effects, a significant issue stemming from the different times at which data is generated, lead to substantial shifts in the underlying data distribution, thus complicating reliable differentiation between cell lines from distinct batch cultures. To address this challenge, we introduce CLANet, a pioneering framework for cross-batch cell line identification using brightfield images, specifically designed to tackle three distinct bio-batch effects. We propose a cell cluster-level selection method to efficiently capture cell density variations, and a self-supervised learning strategy to manage image quality variations, thus producing reliable patch representations. Additionally, we adopt multiple instance learning(MIL) for effective aggregation of instance-level features for cell line identification. Our innovative time-series segment sampling module further enhances MIL's feature-learning capabilities, mitigating biases from varying incubation times across batches. We validate CLANet using data from 32 cell lines across 93 experimental bio-batches from the AstraZeneca Global Cell Bank. Our results show that CLANet outperforms related approaches (e.g. domain adaptation, MIL), demonstrating its effectiveness in addressing bio-batch effects in cell line identification.
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Affiliation(s)
- Lei Tong
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK; Data Sciences and Quantitative Biology, Discovery Sciences, AstraZeneca R&D, Cambridge, UK
| | - Adam Corrigan
- Data Sciences and Quantitative Biology, Discovery Sciences, AstraZeneca R&D, Cambridge, UK
| | - Navin Rathna Kumar
- UK Cell Culture and Banking, Discovery Sciences, AstraZeneca R&D, Alderley Park, UK
| | - Kerry Hallbrook
- UK Cell Culture and Banking, Discovery Sciences, AstraZeneca R&D, Alderley Park, UK
| | - Jonathan Orme
- UK Cell Culture and Banking, Discovery Sciences, AstraZeneca R&D, Cambridge, UK
| | - Yinhai Wang
- Data Sciences and Quantitative Biology, Discovery Sciences, AstraZeneca R&D, Cambridge, UK.
| | - Huiyu Zhou
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK.
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29
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De Wispelaere W, Annibali D, Tuyaerts S, Messiaen J, Antoranz A, Shankar G, Dubroja N, Herreros‐Pomares A, Baiden‐Amissah REM, Orban M, Delfini M, Berardi E, Van Brussel T, Schepers R, Philips G, Boeckx B, Baietti MF, Congedo L, HoWangYin KY, Bayon E, Van Rompuy A, Leucci E, Tabruyn SP, Bosisio F, Mazzone M, Lambrechts D, Amant F. PI3K/mTOR inhibition induces tumour microenvironment remodelling and sensitises pS6 high uterine leiomyosarcoma to PD-1 blockade. Clin Transl Med 2024; 14:e1655. [PMID: 38711203 PMCID: PMC11074386 DOI: 10.1002/ctm2.1655] [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: 10/16/2023] [Revised: 03/24/2024] [Accepted: 03/26/2024] [Indexed: 05/08/2024] Open
Abstract
BACKGROUND Uterine leiomyosarcomas (uLMS) are aggressive tumours with poor prognosis and limited treatment options. Although immune checkpoint blockade (ICB) has proven effective in some 'challenging-to-treat' cancers, clinical trials showed that uLMS do not respond to ICB. Emerging evidence suggests that aberrant PI3K/mTOR signalling can drive resistance to ICB. We therefore explored the relevance of the PI3K/mTOR pathway for ICB treatment in uLMS and explored pharmacological inhibition of this pathway to sensitise these tumours to ICB. METHODS We performed an integrated multiomics analysis based on TCGA data to explore the correlation between PI3K/mTOR dysregulation and immune infiltration in 101 LMS. We assessed response to PI3K/mTOR inhibitors in immunodeficient and humanized uLMS patient-derived xenografts (PDXs) by evaluating tumour microenvironment modulation using multiplex immunofluorescence. We explored response to single-agent and a combination of PI3K/mTOR inhibitors with PD-1 blockade in humanized uLMS PDXs. We mapped intratumoural dynamics using single-cell RNA/TCR sequencing of serially collected biopsies. RESULTS PI3K/mTOR over-activation (pS6high) associated with lymphocyte depletion and wound healing immune landscapes in (u)LMS, suggesting it contributes to immune evasion. In contrast, PI3K/mTOR inhibition induced profound tumour microenvironment remodelling in an ICB-resistant humanized uLMS PDX model, fostering adaptive anti-tumour immune responses. Indeed, PI3K/mTOR inhibition induced macrophage repolarisation towards an anti-tumourigenic phenotype and increased antigen presentation on dendritic and tumour cells, but also promoted infiltration of PD-1+ T cells displaying an exhausted phenotype. When combined with anti-PD-1, PI3K/mTOR inhibition led to partial or complete tumour responses, whereas no response to single-agent anti-PD-1 was observed. Combination therapy reinvigorated exhausted T cells and induced clonal hyper-expansion of a cytotoxic CD8+ T-cell population supported by a CD4+ Th1 niche. CONCLUSIONS Our findings indicate that aberrant PI3K/mTOR pathway activation contributes to immune escape in uLMS and provides a rationale for combining PI3K/mTOR inhibition with ICB for the treatment of this patient population.
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Affiliation(s)
- Wout De Wispelaere
- Department of OncologyLaboratory of Gynecological OncologyUniversity of LeuvenLeuvenBelgium
- Department of Human GeneticsLaboratory for Translational GeneticsUniversity of LeuvenLeuvenBelgium
- Laboratory for Translational GeneticsCenter for Cancer Biology (CCB)Flemish Institute of Biotechnology (VIB)LeuvenBelgium
| | - Daniela Annibali
- Department of OncologyLaboratory of Gynecological OncologyUniversity of LeuvenLeuvenBelgium
- Department of Gynecological OncologyAntoni Van Leeuwenhoek – Netherlands Cancer InstituteAmsterdamThe Netherlands
| | - Sandra Tuyaerts
- Department of Medical OncologyLaboratory of Medical and Molecular Oncology (LMMO)Vrije Universiteit Brussel – UZ BrusselBrusselsBelgium
| | - Julie Messiaen
- Department of Imaging and PathologyTranslational Cell and Tissue ResearchUniversity of LeuvenLeuvenBelgium
- Department of PediatricsUniversity Hospitals LeuvenLeuvenBelgium
| | - Asier Antoranz
- Department of Imaging and PathologyTranslational Cell and Tissue ResearchUniversity of LeuvenLeuvenBelgium
| | - Gautam Shankar
- Department of Imaging and PathologyTranslational Cell and Tissue ResearchUniversity of LeuvenLeuvenBelgium
| | - Nikolina Dubroja
- Department of Imaging and PathologyTranslational Cell and Tissue ResearchUniversity of LeuvenLeuvenBelgium
| | - Alejandro Herreros‐Pomares
- Department of OncologyLaboratory of Gynecological OncologyUniversity of LeuvenLeuvenBelgium
- Department of BiotechnologyUniversitat Politècnica de ValenciaValenciaSpain
| | | | - Marie‐Pauline Orban
- Laboratory of Tumor Inflammation and AngiogenesisCenter for Cancer Biology (CCB)Flemish Institute of Biotechnology (VIB)LeuvenBelgium
- Department of OncologyLaboratory of Tumor Inflammation and AngiogenesisCenter for Cancer Biology (CCB)University of LeuvenLeuvenBelgium
| | - Marcello Delfini
- Laboratory of Tumor Inflammation and AngiogenesisCenter for Cancer Biology (CCB)Flemish Institute of Biotechnology (VIB)LeuvenBelgium
- Department of OncologyLaboratory of Tumor Inflammation and AngiogenesisCenter for Cancer Biology (CCB)University of LeuvenLeuvenBelgium
| | - Emanuele Berardi
- Department of Development and RegenerationLaboratory of Tissue EngineeringUniversity of LeuvenKortrijkBelgium
| | - Thomas Van Brussel
- Department of Human GeneticsLaboratory for Translational GeneticsUniversity of LeuvenLeuvenBelgium
- Laboratory for Translational GeneticsCenter for Cancer Biology (CCB)Flemish Institute of Biotechnology (VIB)LeuvenBelgium
| | - Rogier Schepers
- Department of Human GeneticsLaboratory for Translational GeneticsUniversity of LeuvenLeuvenBelgium
- Laboratory for Translational GeneticsCenter for Cancer Biology (CCB)Flemish Institute of Biotechnology (VIB)LeuvenBelgium
| | - Gino Philips
- Department of Human GeneticsLaboratory for Translational GeneticsUniversity of LeuvenLeuvenBelgium
- Laboratory for Translational GeneticsCenter for Cancer Biology (CCB)Flemish Institute of Biotechnology (VIB)LeuvenBelgium
| | - Bram Boeckx
- Department of Human GeneticsLaboratory for Translational GeneticsUniversity of LeuvenLeuvenBelgium
- Laboratory for Translational GeneticsCenter for Cancer Biology (CCB)Flemish Institute of Biotechnology (VIB)LeuvenBelgium
| | | | - Luigi Congedo
- Department of OncologyLaboratory of Gynecological OncologyUniversity of LeuvenLeuvenBelgium
| | | | | | | | - Eleonora Leucci
- TRACE, Department of OncologyUniversity of LeuvenLeuvenBelgium
| | | | - Francesca Bosisio
- Department of Imaging and PathologyTranslational Cell and Tissue ResearchUniversity of LeuvenLeuvenBelgium
| | - Massimiliano Mazzone
- Laboratory of Tumor Inflammation and AngiogenesisCenter for Cancer Biology (CCB)Flemish Institute of Biotechnology (VIB)LeuvenBelgium
- Department of OncologyLaboratory of Tumor Inflammation and AngiogenesisCenter for Cancer Biology (CCB)University of LeuvenLeuvenBelgium
| | - Diether Lambrechts
- Department of Human GeneticsLaboratory for Translational GeneticsUniversity of LeuvenLeuvenBelgium
- Laboratory for Translational GeneticsCenter for Cancer Biology (CCB)Flemish Institute of Biotechnology (VIB)LeuvenBelgium
| | - Frédéric Amant
- Department of OncologyLaboratory of Gynecological OncologyUniversity of LeuvenLeuvenBelgium
- Department of Gynecological OncologyAntoni Van Leeuwenhoek – Netherlands Cancer InstituteAmsterdamThe Netherlands
- Department of Obstetrics and GynecologyUniversity Hospitals LeuvenLeuvenBelgium
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30
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Houbaert D, Nikolakopoulos AP, Jacobs KA, Meçe O, Roels J, Shankar G, Agrawal M, More S, Ganne M, Rillaerts K, Boon L, Swoboda M, Nobis M, Mourao L, Bosisio F, Vandamme N, Bergers G, Scheele CLGJ, Agostinis P. An autophagy program that promotes T cell egress from the lymph node controls responses to immune checkpoint blockade. Cell Rep 2024; 43:114020. [PMID: 38554280 DOI: 10.1016/j.celrep.2024.114020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 12/21/2023] [Accepted: 03/15/2024] [Indexed: 04/01/2024] Open
Abstract
Lymphatic endothelial cells (LECs) of the lymph node (LN) parenchyma orchestrate leukocyte trafficking and peripheral T cell dynamics. T cell responses to immunotherapy largely rely on peripheral T cell recruitment in tumors. Yet, a systematic and molecular understanding of how LECs within the LNs control T cell dynamics under steady-state and tumor-bearing conditions is lacking. Intravital imaging combined with immune phenotyping shows that LEC-specific deletion of the essential autophagy gene Atg5 alters intranodal positioning of lymphocytes and accrues their persistence in the LNs by increasing the availability of the main egress signal sphingosine-1-phosphate. Single-cell RNA sequencing of tumor-draining LNs shows that loss of ATG5 remodels niche-specific LEC phenotypes involved in molecular pathways regulating lymphocyte trafficking and LEC-T cell interactions. Functionally, loss of LEC autophagy prevents recruitment of tumor-infiltrating T and natural killer cells and abrogates response to immunotherapy. Thus, an LEC-autophagy program boosts immune-checkpoint responses by guiding systemic T cell dynamics.
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Affiliation(s)
- Diede Houbaert
- Cell Death Research and Therapy Group, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium; VIB Center for Cancer Biology Research (CCB), Leuven, Belgium
| | - Apostolos Panagiotis Nikolakopoulos
- Cell Death Research and Therapy Group, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium; VIB Center for Cancer Biology Research (CCB), Leuven, Belgium; Laboratory of Intravital Microscopy and Dynamics of Tumor Progression, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Kathryn A Jacobs
- Cell Death Research and Therapy Group, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium; VIB Center for Cancer Biology Research (CCB), Leuven, Belgium; Laboratory of Tumor Microenvironment and Therapeutic Resistance, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Odeta Meçe
- Cell Death Research and Therapy Group, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium; VIB Center for Cancer Biology Research (CCB), Leuven, Belgium
| | - Jana Roels
- VIB Center for Cancer Biology Research (CCB), Leuven, Belgium; VIB Single Cell Core, Leuven, Belgium
| | - Gautam Shankar
- Laboratory of Translational Cell and Tissue Research, Department of Pathology, KU Leuven and UZ Leuven, Leuven, Belgium
| | - Madhur Agrawal
- Cell Death Research and Therapy Group, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium; VIB Center for Cancer Biology Research (CCB), Leuven, Belgium
| | - Sanket More
- Cell Death Research and Therapy Group, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium; VIB Center for Cancer Biology Research (CCB), Leuven, Belgium
| | - Maarten Ganne
- Cell Death Research and Therapy Group, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium; VIB Center for Cancer Biology Research (CCB), Leuven, Belgium
| | - Kristine Rillaerts
- Cell Death Research and Therapy Group, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium; VIB Center for Cancer Biology Research (CCB), Leuven, Belgium
| | | | - Magdalena Swoboda
- Cell Death Research and Therapy Group, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium; VIB Center for Cancer Biology Research (CCB), Leuven, Belgium
| | - Max Nobis
- Intravital Imaging Expertise Center, VIB-CCB, Leuven, Belgium
| | - Larissa Mourao
- VIB Center for Cancer Biology Research (CCB), Leuven, Belgium; Laboratory of Intravital Microscopy and Dynamics of Tumor Progression, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Francesca Bosisio
- Laboratory of Translational Cell and Tissue Research, Department of Pathology, KU Leuven and UZ Leuven, Leuven, Belgium
| | - Niels Vandamme
- VIB Center for Cancer Biology Research (CCB), Leuven, Belgium; VIB Single Cell Core, Leuven, Belgium
| | - Gabriele Bergers
- VIB Center for Cancer Biology Research (CCB), Leuven, Belgium; Laboratory of Tumor Microenvironment and Therapeutic Resistance, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Colinda L G J Scheele
- VIB Center for Cancer Biology Research (CCB), Leuven, Belgium; Laboratory of Intravital Microscopy and Dynamics of Tumor Progression, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Patrizia Agostinis
- Cell Death Research and Therapy Group, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium; VIB Center for Cancer Biology Research (CCB), Leuven, Belgium.
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Thomas JR, Shelton C, Murphy J, Brittain S, Bray MA, Aspesi P, Concannon J, King FJ, Ihry RJ, Ho DJ, Henault M, Hadjikyriacou A, Neri M, Sigoillot FD, Pham HT, Shum M, Barys L, Jones MD, Martin EJ, Blechschmidt A, Rieffel S, Troxler TJ, Mapa FA, Jenkins JL, Jain RK, Kutchukian PS, Schirle M, Renner S. Enhancing the Small-Scale Screenable Biological Space beyond Known Chemogenomics Libraries with Gray Chemical Matter─Compounds with Novel Mechanisms from High-Throughput Screening Profiles. ACS Chem Biol 2024; 19:938-952. [PMID: 38565185 PMCID: PMC11040606 DOI: 10.1021/acschembio.3c00737] [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: 12/05/2023] [Revised: 02/28/2024] [Accepted: 03/01/2024] [Indexed: 04/04/2024]
Abstract
Phenotypic assays have become an established approach to drug discovery. Greater disease relevance is often achieved through cellular models with increased complexity and more detailed readouts, such as gene expression or advanced imaging. However, the intricate nature and cost of these assays impose limitations on their screening capacity, often restricting screens to well-characterized small compound sets such as chemogenomics libraries. Here, we outline a cheminformatics approach to identify a small set of compounds with likely novel mechanisms of action (MoAs), expanding the MoA search space for throughput limited phenotypic assays. Our approach is based on mining existing large-scale, phenotypic high-throughput screening (HTS) data. It enables the identification of chemotypes that exhibit selectivity across multiple cell-based assays, which are characterized by persistent and broad structure activity relationships (SAR). We validate the effectiveness of our approach in broad cellular profiling assays (Cell Painting, DRUG-seq, and Promotor Signature Profiling) and chemical proteomics experiments. These experiments revealed that the compounds behave similarly to known chemogenetic libraries, but with a notable bias toward novel protein targets. To foster collaboration and advance research in this area, we have curated a public set of such compounds based on the PubChem BioAssay dataset and made it available for use by the scientific community.
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Affiliation(s)
- Jason R. Thomas
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Claude Shelton
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Jason Murphy
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Scott Brittain
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Mark-Anthony Bray
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Peter Aspesi
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - John Concannon
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Frederick J. King
- Novartis
Biomedical Research, San Diego, California 92121, United States
| | - Robert J. Ihry
- Novartis
Biomedical Research, San Diego, California 92121, United States
| | - Daniel J. Ho
- Novartis
Biomedical Research, San Diego, California 92121, United States
| | - Martin Henault
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | | | - Marilisa Neri
- Novartis
Biomedical Research, Basel 4056, Switzerland
| | | | - Helen T. Pham
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Matthew Shum
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Louise Barys
- Novartis
Biomedical Research, Basel 4056, Switzerland
| | - Michael D. Jones
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Eric J. Martin
- Novartis
Biomedical Research, Emeryville, California 94608, United States
| | | | | | | | - Felipa A. Mapa
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Jeremy L. Jenkins
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Rishi K. Jain
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | | | - Markus Schirle
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
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32
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Greatbatch CJ, Lu Q, Hung S, Barnett AJ, Wing K, Liang H, Han X, Zhou T, Siggs OM, Mackey DA, Cook AL, Senabouth A, Liu GS, Craig JE, MacGregor S, Powell JE, Hewitt AW. High throughput functional profiling of genes at intraocular pressure loci reveals distinct networks for glaucoma. Hum Mol Genet 2024; 33:739-751. [PMID: 38272457 PMCID: PMC11031357 DOI: 10.1093/hmg/ddae003] [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: 10/15/2023] [Revised: 12/18/2023] [Accepted: 04/06/2024] [Indexed: 01/27/2024] Open
Abstract
INTRODUCTION Primary open angle glaucoma (POAG) is a leading cause of blindness globally. Characterized by progressive retinal ganglion cell degeneration, the precise pathogenesis remains unknown. Genome-wide association studies (GWAS) have uncovered many genetic variants associated with elevated intraocular pressure (IOP), one of the key risk factors for POAG. We aimed to identify genetic and morphological variation that can be attributed to trabecular meshwork cell (TMC) dysfunction and raised IOP in POAG. METHODS 62 genes across 55 loci were knocked-out in a primary human TMC line. Each knockout group, including five non-targeting control groups, underwent single-cell RNA-sequencing (scRNA-seq) for differentially-expressed gene (DEG) analysis. Multiplexed fluorescence coupled with CellProfiler image analysis allowed for single-cell morphological profiling. RESULTS Many gene knockouts invoked DEGs relating to matrix metalloproteinases and interferon-induced proteins. We have prioritized genes at four loci of interest to identify gene knockouts that may contribute to the pathogenesis of POAG, including ANGPTL2, LMX1B, CAV1, and KREMEN1. Three genetic networks of gene knockouts with similar transcriptomic profiles were identified, suggesting a synergistic function in trabecular meshwork cell physiology. TEK knockout caused significant upregulation of nuclear granularity on morphological analysis, while knockout of TRIOBP, TMCO1 and PLEKHA7 increased granularity and intensity of actin and the cell-membrane. CONCLUSION High-throughput analysis of cellular structure and function through multiplex fluorescent single-cell analysis and scRNA-seq assays enabled the direct study of genetic perturbations at the single-cell resolution. This work provides a framework for investigating the role of genes in the pathogenesis of glaucoma and heterogenous diseases with a strong genetic basis.
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Affiliation(s)
- Connor J Greatbatch
- Menzies Institute for Medical Research, University of Tasmania, 17 Liverpool Street, Hobart, Tasmania 7000, Australia
| | - Qinyi Lu
- Menzies Institute for Medical Research, University of Tasmania, 17 Liverpool Street, Hobart, Tasmania 7000, Australia
| | - Sandy Hung
- Centre for Eye Research Australia, University of Melbourne, Royal Victorian Eye and Ear Hospital, 32 Gisborne St, East Melbourne 3002, Australia
| | - Alexander J Barnett
- Menzies Institute for Medical Research, University of Tasmania, 17 Liverpool Street, Hobart, Tasmania 7000, Australia
| | - Kristof Wing
- Menzies Institute for Medical Research, University of Tasmania, 17 Liverpool Street, Hobart, Tasmania 7000, Australia
| | - Helena Liang
- Centre for Eye Research Australia, University of Melbourne, Royal Victorian Eye and Ear Hospital, 32 Gisborne St, East Melbourne 3002, Australia
| | - Xikun Han
- QIMR Berghofer Medical Research Institute, 300 Herston Rd, Herston, Brisbane 4006, Australia
| | - Tiger Zhou
- Department of Ophthalmology, Flinders University, Flinders Medical Centre, 1 Flinders Dr, Bedford Park, South Australia 5042, Australia
| | - Owen M Siggs
- Garvan Institute of Medical Research, 384 Victoria St, Darlinghurst, Sydney, NSW 2010, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, Short Street, St George Hospital KOGARAH UNSW, Sydney 2217, Australia
| | - David A Mackey
- Menzies Institute for Medical Research, University of Tasmania, 17 Liverpool Street, Hobart, Tasmania 7000, Australia
- Lions Eye Institute, Centre for Vision Sciences, University of Western Australia, 2 Verdun Street Nedlands WA 6009, Australia
| | - Anthony L Cook
- Wicking Dementia Research and Education Centre, University of Tasmania, 17 Liverpool Street, Hobart, TAS 7000, Australia
| | - Anne Senabouth
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, 384 Victoria St, Darlinghurst, Sydney, NSW 2010, Australia
| | - Guei-Sheung Liu
- Menzies Institute for Medical Research, University of Tasmania, 17 Liverpool Street, Hobart, Tasmania 7000, Australia
| | - Jamie E Craig
- Department of Ophthalmology, Flinders University, Flinders Medical Centre, 1 Flinders Dr, Bedford Park, South Australia 5042, Australia
| | - Stuart MacGregor
- QIMR Berghofer Medical Research Institute, 300 Herston Rd, Herston, Brisbane 4006, Australia
| | - Joseph E Powell
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, 384 Victoria St, Darlinghurst, Sydney, NSW 2010, Australia
- UNSW Cellular Genomics Futures Institute, University of New South Wales, 384 Victoria St, Darlinghurst, Sydney, NSW 2010, Australia
| | - Alex W Hewitt
- Menzies Institute for Medical Research, University of Tasmania, 17 Liverpool Street, Hobart, Tasmania 7000, Australia
- Centre for Eye Research Australia, University of Melbourne, Royal Victorian Eye and Ear Hospital, 32 Gisborne St, East Melbourne 3002, Australia
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Augenstreich J, Poddar A, Belew AT, El-Sayed NM, Briken V. da_Tracker: Automated workflow for high throughput single cell and single phagosome tracking in infected cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.10.588863. [PMID: 38645070 PMCID: PMC11030405 DOI: 10.1101/2024.04.10.588863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Time-lapse microscopy has emerged as a crucial tool in cell biology, facilitating a deeper understanding of dynamic cellular processes. While existing tracking tools have proven effective in detecting and monitoring objects over time, the quantification of signals within these tracked objects often faces implementation constraints. In the context of infectious diseases, the quantification of signals at localized compartments within the cell and around intracellular pathogens can provide even deeper insight into the interactions between the pathogen and host cell organelles. Existing quantitative analysis at a single-phagosome level remains limited and dependent on manual tracking methods. We developed a near-fully automated workflow that performs with limited bias, high-throughput cell segmentation and quantitative tracking of both single cell and single bacterium/phagosome within multi-channel, z-stack, time-lapse confocal microscopy videos. We took advantage of the PyImageJ library to bring Fiji functionality into a Python environment and combined deep-learning-based segmentation from Cellpose with tracking algorithms from Trackmate. Our workflow provides a versatile toolkit of functions for measuring relevant signal parameters at the single-cell level (such as velocity or bacterial burden) and at the single-phagosome level (i.e. assessment of phagosome maturation over time). It's capabilities in both single-cell and single-phagosome quantification, its flexibility and open-source nature should assist studies that aim to decipher for example the pathogenicity of bacteria and the mechanism of virulence factors that could pave the way for the development of innovative therapeutic approaches.
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Affiliation(s)
- Jacques Augenstreich
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742 USA
| | - Anushka Poddar
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742 USA
| | - Ashton T. Belew
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742 USA
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742 USA
| | - Najib M El-Sayed
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742 USA
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742 USA
| | - Volker Briken
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742 USA
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Kalinin AA, Arevalo J, Vulliard L, Serrano E, Tsang H, Bornholdt M, Rajwa B, Carpenter AE, Way GP, Singh S. A versatile information retrieval framework for evaluating profile strength and similarity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.01.587631. [PMID: 38617315 PMCID: PMC11014546 DOI: 10.1101/2024.04.01.587631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
In profiling assays, thousands of biological properties are measured in a single test, yielding biological discoveries by capturing the state of a cell population, often at the single-cell level. However, for profiling datasets, it has been challenging to evaluate the phenotypic activity of a sample and the phenotypic consistency among samples, due to profiles' high dimensionality, heterogeneous nature, and non-linear properties. Existing methods leave researchers uncertain where to draw boundaries between meaningful biological response and technical noise. Here, we developed a statistical framework that uses the well-established mean average precision (mAP) as a single, data-driven metric to bridge this gap. We validated the mAP framework against established metrics through simulations and real-world data applications, revealing its ability to capture subtle and meaningful biological differences in cell state. Specifically, we used mAP to assess both phenotypic activity for a given perturbation (or a sample) as well as consistency within groups of perturbations (or samples) across diverse high-dimensional datasets. We evaluated the framework on different profile types (image, protein, and mRNA profiles), perturbation types (CRISPR gene editing, gene overexpression, and small molecules), and profile resolutions (single-cell and bulk). Our open-source software allows this framework to be applied to identify interesting biological phenomena and promising therapeutics from large-scale profiling data.
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Affiliation(s)
| | - John Arevalo
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | - Loan Vulliard
- Systems Immunology and Single-Cell Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Erik Serrano
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora CO, USA
| | - Hillary Tsang
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | - Michael Bornholdt
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | - Bartek Rajwa
- Bindley Bioscience Center, Purdue University, West Lafayette IN, USA
| | - Anne E. Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | - Gregory P. Way
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora CO, USA
| | - Shantanu Singh
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
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35
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Stossi F, Singh PK, Marini M, Safari K, Szafran AT, Tostado AR, Candler CD, Mancini MG, Mosa EA, Bolt MJ, Labate D, Mancini MA. SPACe (Swift Phenotypic Analysis of Cells): an open-source, single cell analysis of Cell Painting data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.21.586132. [PMID: 38585902 PMCID: PMC10996526 DOI: 10.1101/2024.03.21.586132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Phenotypic profiling by high throughput microscopy has become one of the leading tools for screening large sets of perturbations in cellular models. Of the numerous methods used over the years, the flexible and economical Cell Painting (CP) assay has been central in the field, allowing for large screening campaigns leading to a vast number of data-rich images. Currently, to analyze data of this scale, available open-source software ( i.e. , CellProfiler) requires computational resources that are not available to most laboratories worldwide. In addition, the image-embedded cell-to-cell variation of responses within a population, while collected and analyzed, is usually averaged and unused. Here we introduce SPACe ( S wift P henotypic A nalysis of Ce lls), an open source, Python-based platform for the analysis of single cell image-based morphological profiles produced by CP experiments. SPACe can process a typical dataset approximately ten times faster than CellProfiler on common desktop computers without loss in mechanism of action (MOA) recognition accuracy. It also computes directional distribution-based distances (Earth Mover's Distance - EMD) of morphological features for quality control and hit calling. We highlight several advantages of SPACe analysis on CP assays, including reproducibility across multiple biological replicates, easy applicability to multiple (∼20) cell lines, sensitivity to variable cell-to-cell responses, and biological interpretability to explain image-based features. We ultimately illustrate the advantages of SPACe in a screening campaign of cell metabolism small molecule inhibitors which we performed in seven cell lines to highlight the importance of testing perturbations across models.
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36
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Ounissi M, Latouche M, Racoceanu D. PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies. Sci Rep 2024; 14:6482. [PMID: 38499658 PMCID: PMC10948879 DOI: 10.1038/s41598-024-56081-7] [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: 04/13/2023] [Accepted: 03/01/2024] [Indexed: 03/20/2024] Open
Abstract
Quantifying the phagocytosis of dynamic, unstained cells is essential for evaluating neurodegenerative diseases. However, measuring rapid cell interactions and distinguishing cells from background make this task very challenging when processing time-lapse phase-contrast video microscopy. In this study, we introduce an end-to-end, scalable, and versatile real-time framework for quantifying and analyzing phagocytic activity. Our proposed pipeline is able to process large data-sets and includes a data quality verification module to counteract potential perturbations such as microscope movements and frame blurring. We also propose an explainable cell segmentation module to improve the interpretability of deep learning methods compared to black-box algorithms. This includes two interpretable deep learning capabilities: visual explanation and model simplification. We demonstrate that interpretability in deep learning is not the opposite of high performance, by additionally providing essential deep learning algorithm optimization insights and solutions. Besides, incorporating interpretable modules results in an efficient architecture design and optimized execution time. We apply this pipeline to quantify and analyze microglial cell phagocytosis in frontotemporal dementia (FTD) and obtain statistically reliable results showing that FTD mutant cells are larger and more aggressive than control cells. The method has been tested and validated on several public benchmarks by generating state-of-the art performances. To stimulate translational approaches and future studies, we release an open-source end-to-end pipeline and a unique microglial cells phagocytosis dataset for immune system characterization in neurodegenerative diseases research. This pipeline and the associated dataset will consistently crystallize future advances in this field, promoting the development of efficient and effective interpretable algorithms dedicated to the critical domain of neurodegenerative diseases' characterization. https://github.com/ounissimehdi/PhagoStat .
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Affiliation(s)
- Mehdi Ounissi
- CNRS, Inserm, AP-HP, Inria, Paris Brain Institute-ICM, Sorbonne University, 75013, Paris, France
| | - Morwena Latouche
- Inserm, CNRS, AP-HP, Institut du Cerveau, ICM, Sorbonne Université, 75013, Paris, France
- PSL Research university, EPHE, Paris, France
| | - Daniel Racoceanu
- CNRS, Inserm, AP-HP, Inria, Paris Brain Institute-ICM, Sorbonne University, 75013, Paris, France.
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37
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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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38
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Margiotta-Casaluci L, Owen SF, Winter MJ. Cross-Species Extrapolation of Biological Data to Guide the Environmental Safety Assessment of Pharmaceuticals-The State of the Art and Future Priorities. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2024; 43:513-525. [PMID: 37067359 DOI: 10.1002/etc.5634] [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: 12/21/2022] [Revised: 03/23/2023] [Accepted: 04/13/2023] [Indexed: 05/27/2023]
Abstract
The extrapolation of biological data across species is a key aspect of biomedical research and drug development. In this context, comparative biology considerations are applied with the goal of understanding human disease and guiding the development of effective and safe medicines. However, the widespread occurrence of pharmaceuticals in the environment and the need to assess the risk posed to wildlife have prompted a renewed interest in the extrapolation of pharmacological and toxicological data across the entire tree of life. To address this challenge, a biological "read-across" approach, based on the use of mammalian data to inform toxicity predictions in wildlife species, has been proposed as an effective way to streamline the environmental safety assessment of pharmaceuticals. Yet, how effective has this approach been, and are we any closer to being able to accurately predict environmental risk based on known human risk? We discuss the main theoretical and experimental advancements achieved in the last 10 years of research in this field. We propose that a better understanding of the functional conservation of drug targets across species and of the quantitative relationship between target modulation and adverse effects should be considered as future research priorities. This pharmacodynamic focus should be complemented with the application of higher-throughput experimental and computational approaches to accelerate the prediction of internal exposure dynamics. The translation of comparative (eco)toxicology research into real-world applications, however, relies on the (limited) availability of experts with the skill set needed to navigate the complexity of the problem; hence, we also call for synergistic multistakeholder efforts to support and strengthen comparative toxicology research and education at a global level. Environ Toxicol Chem 2024;43:513-525. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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Affiliation(s)
- Luigi Margiotta-Casaluci
- Institute of Pharmaceutical Science, Faculty of Life Sciences & Medicine, King's College London, London, United Kingdom
| | - Stewart F Owen
- Global Sustainability, AstraZeneca, Macclesfield, Cheshire, United Kingdom
| | - Matthew J Winter
- Biosciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, Devon, United Kingdom
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Miller SG, Hoh M, Ebmeier CC, Tay JW, Ahn NG. Cooperative polarization of MCAM/CD146 and ERM family proteins in melanoma. Mol Biol Cell 2024; 35:ar31. [PMID: 38117590 PMCID: PMC10916866 DOI: 10.1091/mbc.e23-06-0255] [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: 06/28/2023] [Revised: 11/22/2023] [Accepted: 12/15/2023] [Indexed: 12/22/2023] Open
Abstract
The WRAMP structure is a protein network associated with tail-end actomyosin contractility, membrane retraction, and directional persistence during cell migration. A marker of WRAMP structures is melanoma cell adhesion molecule (MCAM) which dynamically polarizes to the cell rear. However, factors that mediate MCAM polarization are still unknown. In this study, BioID using MCAM as bait identifies the ERM family proteins, moesin, ezrin, and radixin, as WRAMP structure components. We also present a novel image analysis pipeline, Protein Polarity by Percentile ("3P"), which classifies protein polarization using machine learning and facilitates quantitative analysis. Using 3P, we find that depletion of moesin, and to a lesser extent ezrin, decreases the proportion of cells with polarized MCAM. Furthermore, although copolarized MCAM and ERM proteins show high spatial overlap, 3P identifies subpopulations with ERM proteins closer to the cell periphery. Live-cell imaging confirms that MCAM and ERM protein polarization is tightly coordinated, but ERM proteins enrich at the cell edge first. Finally, deletion of a juxtamembrane segment in MCAM previously shown to promote ERM protein interactions impedes MCAM polarization. Our findings highlight the requirement for ERM proteins in recruitment of MCAM to WRAMP structures and an advanced computational tool to characterize protein polarization.
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Affiliation(s)
- Suzannah G. Miller
- Department of Biochemistry, University of Colorado Boulder, Boulder CO 80303
| | - Maria Hoh
- Department of Biochemistry, University of Colorado Boulder, Boulder CO 80303
| | | | - Jian Wei Tay
- BioFrontiers Institute, University of Colorado Boulder, Boulder CO 80303
| | - Natalie G. Ahn
- Department of Biochemistry, University of Colorado Boulder, Boulder CO 80303
- BioFrontiers Institute, University of Colorado Boulder, Boulder CO 80303
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40
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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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41
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Arevalo J, Su E, van Dijk R, Carpenter AE, Singh S. Evaluating batch correction methods for image-based cell profiling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.15.558001. [PMID: 37745478 PMCID: PMC10516049 DOI: 10.1101/2023.09.15.558001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
High-throughput image-based profiling platforms are powerful technologies capable of collecting data from billions of cells exposed to thousands of perturbations in a time- and cost-effective manner. Therefore, image-based profiling data has been increasingly used for diverse biological applications, such as predicting drug mechanism of action or gene function. However, batch effects pose severe limitations to community-wide efforts to integrate and interpret image-based profiling data collected across different laboratories and equipment. To address this problem, we benchmarked seven high-performing scRNA-seq batch correction techniques, representing diverse approaches, using a newly released Cell Painting dataset, the largest publicly accessible image-based dataset. We focused on five different scenarios with varying complexity, and we found that Harmony, a mixture-model based method, consistently outperformed the other tested methods. Our proposed framework, benchmark, and metrics can additionally be used to assess new batch correction methods in the future. Overall, this work paves the way for improvements that allow the community to make best use of public Cell Painting data for scientific discovery.
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Affiliation(s)
- John Arevalo
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Ellen Su
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Robert van Dijk
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Shantanu Singh
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
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42
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Bender SWB, Dreisler MW, Zhang M, Kæstel-Hansen J, Hatzakis NS. SEMORE: SEgmentation and MORphological fingErprinting by machine learning automates super-resolution data analysis. Nat Commun 2024; 15:1763. [PMID: 38409214 PMCID: PMC10897458 DOI: 10.1038/s41467-024-46106-0] [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: 05/10/2023] [Accepted: 02/13/2024] [Indexed: 02/28/2024] Open
Abstract
The morphology of protein assemblies impacts their behaviour and contributes to beneficial and aberrant cellular responses. While single-molecule localization microscopy provides the required spatial resolution to investigate these assemblies, the lack of universal robust analytical tools to extract and quantify underlying structures limits this powerful technique. Here we present SEMORE, a semi-automatic machine learning framework for universal, system- and input-dependent, analysis of super-resolution data. SEMORE implements a multi-layered density-based clustering module to dissect biological assemblies and a morphology fingerprinting module for quantification by multiple geometric and kinetics-based descriptors. We demonstrate SEMORE on simulations and diverse raw super-resolution data: time-resolved insulin aggregates, and published data of dSTORM imaging of nuclear pore complexes, fibroblast growth receptor 1, sptPALM of Syntaxin 1a and dynamic live-cell PALM of ryanodine receptors. SEMORE extracts and quantifies all protein assemblies, their temporal morphology evolution and provides quantitative insights, e.g. classification of heterogeneous insulin aggregation pathways and NPC geometry in minutes. SEMORE is a general analysis platform for super-resolution data, and being a time-aware framework can also support the rise of 4D super-resolution data.
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Affiliation(s)
- Steen W B Bender
- Department of Chemistry, University of Copenhagen, Copenhagen, Denmark
- Center for 4D cellular dynamics, University of Copenhagen, Copenhagen, Denmark
- Novo Nordisk Center for Optimised Oligo Escape and Control of Disease, University of Copenhagen, Copenhagen, Denmark
| | - Marcus W Dreisler
- Department of Chemistry, University of Copenhagen, Copenhagen, Denmark
- Center for 4D cellular dynamics, University of Copenhagen, Copenhagen, Denmark
- Novo Nordisk Center for Optimised Oligo Escape and Control of Disease, University of Copenhagen, Copenhagen, Denmark
| | - Min Zhang
- Department of Chemistry, University of Copenhagen, Copenhagen, Denmark
- Center for 4D cellular dynamics, University of Copenhagen, Copenhagen, Denmark
- Novo Nordisk Center for Optimised Oligo Escape and Control of Disease, University of Copenhagen, Copenhagen, Denmark
| | - Jacob Kæstel-Hansen
- Department of Chemistry, University of Copenhagen, Copenhagen, Denmark.
- Center for 4D cellular dynamics, University of Copenhagen, Copenhagen, Denmark.
- Novo Nordisk Center for Optimised Oligo Escape and Control of Disease, University of Copenhagen, Copenhagen, Denmark.
| | - Nikos S Hatzakis
- Department of Chemistry, University of Copenhagen, Copenhagen, Denmark.
- Center for 4D cellular dynamics, University of Copenhagen, Copenhagen, Denmark.
- Novo Nordisk Center for Optimised Oligo Escape and Control of Disease, University of Copenhagen, Copenhagen, Denmark.
- Novo Nordisk Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
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43
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Moshkov N, Bornholdt M, Benoit S, Smith M, McQuin C, Goodman A, Senft RA, Han Y, Babadi M, Horvath P, Cimini BA, Carpenter AE, Singh S, Caicedo JC. Learning representations for image-based profiling of perturbations. Nat Commun 2024; 15:1594. [PMID: 38383513 PMCID: PMC10881515 DOI: 10.1038/s41467-024-45999-1] [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: 10/11/2022] [Accepted: 02/07/2024] [Indexed: 02/23/2024] Open
Abstract
Measuring the phenotypic effect of treatments on cells through imaging assays is an efficient and powerful way of studying cell biology, and requires computational methods for transforming images into quantitative data. Here, we present an improved strategy for learning representations of treatment effects from high-throughput imaging, following a causal interpretation. We use weakly supervised learning for modeling associations between images and treatments, and show that it encodes both confounding factors and phenotypic features in the learned representation. To facilitate their separation, we constructed a large training dataset with images from five different studies to maximize experimental diversity, following insights from our causal analysis. Training a model with this dataset successfully improves downstream performance, and produces a reusable convolutional network for image-based profiling, which we call Cell Painting CNN. We evaluated our strategy on three publicly available Cell Painting datasets, and observed that the Cell Painting CNN improves performance in downstream analysis up to 30% with respect to classical features, while also being more computationally efficient.
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Affiliation(s)
- Nikita Moshkov
- HUN-REN Biological Research Centre, 62 Temesvári krt, Szeged, 6726, Hungary
| | - Michael Bornholdt
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA
| | - Santiago Benoit
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA
- Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA
| | - Matthew Smith
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA
- Harvard College, 86 Brattle Street Cambridge, Cambridge, MA, 02138, USA
| | - Claire McQuin
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA
| | - Allen Goodman
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA
| | - Rebecca A Senft
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA
| | - Yu Han
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA
| | - Mehrtash Babadi
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA
| | - Peter Horvath
- HUN-REN Biological Research Centre, 62 Temesvári krt, Szeged, 6726, Hungary
| | - Beth A Cimini
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA
| | - Anne E Carpenter
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA
| | - Shantanu Singh
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA
| | - Juan C Caicedo
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA.
- Morgridge Institute for Research, 330 N Orchard St, Madison, WI, 53715, USA.
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, 1300 University Ave, Madison, WI, 53706, USA.
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44
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Li H, Seada H, Madnick S, Zhao H, Chen Z, Li F, Zhu F, Hall S, Boekelheide K. Machine learning-assisted high-content imaging analysis of 3D MCF7 microtissues for estrogenic effect prediction. Sci Rep 2024; 14:2999. [PMID: 38316851 PMCID: PMC10844358 DOI: 10.1038/s41598-024-53323-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/30/2024] [Indexed: 02/07/2024] Open
Abstract
Endocrine-disrupting chemicals (EDCs) pose a significant threat to human well-being and the ecosystem. However, in managing the many thousands of uncharacterized chemical entities, the high-throughput screening of EDCs using relevant biological endpoints remains challenging. Three-dimensional (3D) culture technology enables the development of more physiologically relevant systems in more realistic biochemical microenvironments. The high-content and quantitative imaging techniques enable quantifying endpoints associated with cell morphology, cell-cell interaction, and microtissue organization. In the present study, 3D microtissues formed by MCF-7 breast cancer cells were exposed to the model EDCs estradiol (E2) and propyl pyrazole triol (PPT). A 3D imaging and image analysis pipeline was established to extract quantitative image features from estrogen-exposed microtissues. Moreover, a machine-learning classification model was built using estrogenic-associated differential imaging features. Based on 140 common differential image features found between the E2 and PPT group, the classification model predicted E2 and PPT exposure with AUC-ROC at 0.9528 and 0.9513, respectively. Deep learning-assisted analysis software was developed to characterize microtissue gland lumen formation. The fully automated tool can accurately characterize the number of identified lumens and the total luminal volume of each microtissue. Overall, the current study established an integrated approach by combining non-supervised image feature profiling and supervised luminal volume characterization, which reflected the complexity of functional ER signaling and highlighted a promising conceptual framework for estrogenic EDC risk assessment.
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Affiliation(s)
- Hui Li
- College of Pharmaceutical Sciences, Center for Drug Safety Evaluation and Research of Zhejiang University, Zhejiang University, 866 Yuhangtang Rd, Hangzhou, 310058, China.
- Department of Pathology and Laboratory Medicine, Brown University, 70 Ship Street, Providence, RI, 02903, USA.
| | - Haitham Seada
- Department of Pathology and Laboratory Medicine, Brown University, 70 Ship Street, Providence, RI, 02903, USA
| | - Samantha Madnick
- Department of Pathology and Laboratory Medicine, Brown University, 70 Ship Street, Providence, RI, 02903, USA
| | - He Zhao
- College of Pharmaceutical Sciences, Center for Drug Safety Evaluation and Research of Zhejiang University, Zhejiang University, 866 Yuhangtang Rd, Hangzhou, 310058, China
| | - Zhaozeng Chen
- College of Pharmaceutical Sciences, Center for Drug Safety Evaluation and Research of Zhejiang University, Zhejiang University, 866 Yuhangtang Rd, Hangzhou, 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Susan Hall
- Department of Pathology and Laboratory Medicine, Brown University, 70 Ship Street, Providence, RI, 02903, USA
| | - Kim Boekelheide
- Department of Pathology and Laboratory Medicine, Brown University, 70 Ship Street, Providence, RI, 02903, USA.
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45
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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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46
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Gómez-de-Mariscal E, Del Rosario M, Pylvänäinen JW, Jacquemet G, Henriques R. Harnessing artificial intelligence to reduce phototoxicity in live imaging. J Cell Sci 2024; 137:jcs261545. [PMID: 38324353 PMCID: PMC10912813 DOI: 10.1242/jcs.261545] [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] [Indexed: 02/08/2024] Open
Abstract
Fluorescence microscopy is essential for studying living cells, tissues and organisms. However, the fluorescent light that switches on fluorescent molecules also harms the samples, jeopardizing the validity of results - particularly in techniques such as super-resolution microscopy, which demands extended illumination. Artificial intelligence (AI)-enabled software capable of denoising, image restoration, temporal interpolation or cross-modal style transfer has great potential to rescue live imaging data and limit photodamage. Yet we believe the focus should be on maintaining light-induced damage at levels that preserve natural cell behaviour. In this Opinion piece, we argue that a shift in role for AIs is needed - AI should be used to extract rich insights from gentle imaging rather than recover compromised data from harsh illumination. Although AI can enhance imaging, our ultimate goal should be to uncover biological truths, not just retrieve data. It is essential to prioritize minimizing photodamage over merely pushing technical limits. Our approach is aimed towards gentle acquisition and observation of undisturbed living systems, aligning with the essence of live-cell fluorescence microscopy.
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Affiliation(s)
| | | | - Joanna W. Pylvänäinen
- Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, Turku 20500, Finland
| | - Guillaume Jacquemet
- Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, Turku 20500, Finland
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland
- Turku Bioimaging, University of Turku and Åbo Akademi University, Turku 20520, Finland
- InFLAMES Research Flagship Center, Åbo Akademi University, Turku 20100, Finland
| | - Ricardo Henriques
- Instituto Gulbenkian de Ciência, Oeiras 2780-156, Portugal
- UCL Laboratory for Molecular Cell Biology, University College London, London WC1E 6BT, UK
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47
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Jan M, Spangaro A, Lenartowicz M, Mattiazzi Usaj M. From pixels to insights: Machine learning and deep learning for bioimage analysis. Bioessays 2024; 46:e2300114. [PMID: 38058114 DOI: 10.1002/bies.202300114] [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: 06/24/2023] [Revised: 10/25/2023] [Accepted: 11/13/2023] [Indexed: 12/08/2023]
Abstract
Bioimage analysis plays a critical role in extracting information from biological images, enabling deeper insights into cellular structures and processes. The integration of machine learning and deep learning techniques has revolutionized the field, enabling the automated, reproducible, and accurate analysis of biological images. Here, we provide an overview of the history and principles of machine learning and deep learning in the context of bioimage analysis. We discuss the essential steps of the bioimage analysis workflow, emphasizing how machine learning and deep learning have improved preprocessing, segmentation, feature extraction, object tracking, and classification. We provide examples that showcase the application of machine learning and deep learning in bioimage analysis. We examine user-friendly software and tools that enable biologists to leverage these techniques without extensive computational expertise. This review is a resource for researchers seeking to incorporate machine learning and deep learning in their bioimage analysis workflows and enhance their research in this rapidly evolving field.
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Affiliation(s)
- Mahta Jan
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Canada
| | - Allie Spangaro
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Canada
| | - Michelle Lenartowicz
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Canada
| | - Mojca Mattiazzi Usaj
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Canada
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48
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Camunas-Soler J. Integrating single-cell transcriptomics with cellular phenotypes: cell morphology, Ca 2+ imaging and electrophysiology. Biophys Rev 2024; 16:89-107. [PMID: 38495444 PMCID: PMC10937895 DOI: 10.1007/s12551-023-01174-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 11/29/2023] [Indexed: 03/19/2024] Open
Abstract
I review recent technological advancements in coupling single-cell transcriptomics with cellular phenotypes including morphology, calcium signaling, and electrophysiology. Single-cell RNA sequencing (scRNAseq) has revolutionized cell type classifications by capturing the transcriptional diversity of cells. A new wave of methods to integrate scRNAseq and biophysical measurements is facilitating the linkage of transcriptomic data to cellular function, which provides physiological insight into cellular states. I briefly discuss critical factors of these phenotypical characterizations such as timescales, information content, and analytical tools. Dedicated sections focus on the integration with cell morphology, calcium imaging, and electrophysiology (patch-seq), emphasizing their complementary roles. I discuss their application in elucidating cellular states, refining cell type classifications, and uncovering functional differences in cell subtypes. To illustrate the practical applications and benefits of these methods, I highlight their use in tissues with excitable cell-types such as the brain, pancreatic islets, and the retina. The potential of combining functional phenotyping with spatial transcriptomics for a detailed mapping of cell phenotypes in situ is explored. Finally, I discuss open questions and future perspectives, emphasizing the need for a shift towards broader accessibility through increased throughput.
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Affiliation(s)
- Joan Camunas-Soler
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, University of Gothenburg, 405 30 Gothenburg, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden
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49
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Chunarkar-Patil P, Kaleem M, Mishra R, Ray S, Ahmad A, Verma D, Bhayye S, Dubey R, Singh HN, Kumar S. Anticancer Drug Discovery Based on Natural Products: From Computational Approaches to Clinical Studies. Biomedicines 2024; 12:201. [PMID: 38255306 PMCID: PMC10813144 DOI: 10.3390/biomedicines12010201] [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: 12/02/2023] [Revised: 01/01/2024] [Accepted: 01/10/2024] [Indexed: 01/24/2024] Open
Abstract
Globally, malignancies cause one out of six mortalities, which is a serious health problem. Cancer therapy has always been challenging, apart from major advances in immunotherapies, stem cell transplantation, targeted therapies, hormonal therapies, precision medicine, and palliative care, and traditional therapies such as surgery, radiation therapy, and chemotherapy. Natural products are integral to the development of innovative anticancer drugs in cancer research, offering the scientific community the possibility of exploring novel natural compounds against cancers. The role of natural products like Vincristine and Vinblastine has been thoroughly implicated in the management of leukemia and Hodgkin's disease. The computational method is the initial key approach in drug discovery, among various approaches. This review investigates the synergy between natural products and computational techniques, and highlights their significance in the drug discovery process. The transition from computational to experimental validation has been highlighted through in vitro and in vivo studies, with examples such as betulinic acid and withaferin A. The path toward therapeutic applications have been demonstrated through clinical studies of compounds such as silvestrol and artemisinin, from preclinical investigations to clinical trials. This article also addresses the challenges and limitations in the development of natural products as potential anti-cancer drugs. Moreover, the integration of deep learning and artificial intelligence with traditional computational drug discovery methods may be useful for enhancing the anticancer potential of natural products.
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Affiliation(s)
- Pritee Chunarkar-Patil
- Department of Bioinformatics, Rajiv Gandhi Institute of IT and Biotechnology, Bharati Vidyapeeth (Deemed to be University), Pune 411046, Maharashtra, India
| | - Mohammed Kaleem
- Department of Pharmacology, Dadasaheb Balpande, College of Pharmacy, Nagpur 440037, Maharashtra, India;
| | - Richa Mishra
- Department of Computer Engineering, Parul University, Ta. Waghodia, Vadodara 391760, Gujarat, India;
| | - Subhasree Ray
- Department of Life Science, Sharda School of Basic Sciences and Research, Greater Noida 201310, Uttar Pradesh, India
| | - Aftab Ahmad
- Health Information Technology Department, The Applied College, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Pharmacovigilance and Medication Safety Unit, Center of Research Excellence for Drug Research and Pharmaceutical Industries, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Devvret Verma
- Department of Biotechnology, Graphic Era (Deemed to be University), Dehradun 248002, Uttarkhand, India;
| | - Sagar Bhayye
- Department of Bioinformatics, Rajiv Gandhi Institute of IT and Biotechnology, Bharati Vidyapeeth (Deemed to be University), Pune 411046, Maharashtra, India
| | - Rajni Dubey
- Division of Cardiology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei 11031, Taiwan
| | - Himanshu Narayan Singh
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Sanjay Kumar
- Biological and Bio-Computational Lab, Department of Life Science, Sharda School of Basic Sciences and Research, Sharda University, Greater Noida 201310, Uttar Pradesh, India
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50
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Winn-Nuñez ET, Witt H, Bhaskar D, Huang RY, Reichner JS, Wong IY, Crawford L. Generative modeling of biological shapes and images using a probabilistic α-shape sampler. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.09.574919. [PMID: 38260340 PMCID: PMC10802457 DOI: 10.1101/2024.01.09.574919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Understanding morphological variation is an important task in many areas of computational biology. Recent studies have focused on developing computational tools for the task of sub-image selection which aims at identifying structural features that best describe the variation between classes of shapes. A major part in assessing the utility of these approaches is to demonstrate their performance on both simulated and real datasets. However, when creating a model for shape statistics, real data can be difficult to access and the sample sizes for these data are often small due to them being expensive to collect. Meanwhile, the current landscape of generative models for shapes has been mostly limited to approaches that use black-box inference-making it difficult to systematically assess the power and calibration of sub-image models. In this paper, we introduce the α -shape sampler: a probabilistic framework for generating realistic 2D and 3D shapes based on probability distributions which can be learned from real data. We demonstrate our framework using proof-of-concept examples and in two real applications in biology where we generate (i) 2D images of healthy and septic neutrophils and (ii) 3D computed tomography (CT) scans of primate mandibular molars. The α -shape sampler R package is open-source and can be downloaded at https://github.com/lcrawlab/ashapesampler.
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Affiliation(s)
| | - Hadley Witt
- Graduate Program in Pathobiology, Brown University, Providence, RI, USA
- Division of Surgical Research, Department of Surgery, Rhode Island Hospital, Providence, RI, USA
| | | | - Ryan Y. Huang
- Department of Computer Science, Brown University, Providence, RI USA
| | - Jonathan S. Reichner
- Graduate Program in Pathobiology, Brown University, Providence, RI, USA
- Division of Surgical Research, Department of Surgery, Rhode Island Hospital, Providence, RI, USA
| | - Ian Y. Wong
- Graduate Program in Pathobiology, Brown University, Providence, RI, USA
- School of Engineering, Legoretta Cancer Center, Brown University, Providence, RI USA
| | - Lorin Crawford
- Microsoft Research, Cambridge, MA, USA
- Department of Biostatistics, Brown University, Providence, RI, USA
- Center for Computational Molecular Biology, Brown University, Providence, RI, USA
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