1
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Shang R, Luke GP, O'Donnell M. Joint segmentation and image reconstruction with error prediction in photoacoustic imaging using deep learning. PHOTOACOUSTICS 2024; 40:100645. [PMID: 39347464 PMCID: PMC11424948 DOI: 10.1016/j.pacs.2024.100645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Revised: 08/16/2024] [Accepted: 09/10/2024] [Indexed: 10/01/2024]
Abstract
Deep learning has been used to improve photoacoustic (PA) image reconstruction. One major challenge is that errors cannot be quantified to validate predictions when ground truth is unknown. Validation is key to quantitative applications, especially using limited-bandwidth ultrasonic linear detector arrays. Here, we propose a hybrid Bayesian convolutional neural network (Hybrid-BCNN) to jointly predict PA image and segmentation with error (uncertainty) predictions. Each output pixel represents a probability distribution where error can be quantified. The Hybrid-BCNN was trained with simulated PA data and applied to both simulations and experiments. Due to the sparsity of PA images, segmentation focuses Hybrid-BCNN on minimizing the loss function in regions with PA signals for better predictions. The results show that accurate PA segmentations and images are obtained, and error predictions are highly statistically correlated to actual errors. To leverage error predictions, confidence processing created PA images above a specific confidence level.
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Affiliation(s)
- Ruibo Shang
- uWAMIT Center, Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
| | - Geoffrey P Luke
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA
| | - Matthew O'Donnell
- uWAMIT Center, Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
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2
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Saguy A, Nahimov T, Lehrman M, Gómez-de-Mariscal E, Hidalgo-Cenalmor I, Alalouf O, Balakrishnan A, Heilemann M, Henriques R, Shechtman Y. This Microtubule Does Not Exist: Super-Resolution Microscopy Image Generation by a Diffusion Model. SMALL METHODS 2024:e2400672. [PMID: 39400948 DOI: 10.1002/smtd.202400672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 09/07/2024] [Indexed: 10/15/2024]
Abstract
Generative models, such as diffusion models, have made significant advancements in recent years, enabling the synthesis of high-quality realistic data across various domains. Here, the adaptation and training of a diffusion model on super-resolution microscopy images are explored. It is shown that the generated images resemble experimental images, and that the generation process does not exhibit a large degree of memorization from existing images in the training set. To demonstrate the usefulness of the generative model for data augmentation, the performance of a deep learning-based single-image super-resolution (SISR) method trained using generated high-resolution data is compared against training using experimental images alone, or images generated by mathematical modeling. Using a few experimental images, the reconstruction quality and the spatial resolution of the reconstructed images are improved, showcasing the potential of diffusion model image generation for overcoming the limitations accompanying the collection and annotation of microscopy images. Finally, the pipeline is made publicly available, runnable online, and user-friendly to enable researchers to generate their own synthetic microscopy data. This work demonstrates the potential contribution of generative diffusion models for microscopy tasks and paves the way for their future application in this field.
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Affiliation(s)
- Alon Saguy
- Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, 3200001, Israel
| | - Tav Nahimov
- Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, 3200001, Israel
| | - Maia Lehrman
- Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, 3200001, Israel
| | - Estibaliz Gómez-de-Mariscal
- Optical cell biology group, Instituto Gulbenkian de Ciência, Oeiras, 2780-156, Portugal
- Optical cell biology group, Gulbenkian Institute of Molecular Medicine, Oeiras, 2780-156, Portugal
| | - Iván Hidalgo-Cenalmor
- Optical cell biology group, Instituto Gulbenkian de Ciência, Oeiras, 2780-156, Portugal
- Optical cell biology group, Gulbenkian Institute of Molecular Medicine, Oeiras, 2780-156, Portugal
| | - Onit Alalouf
- Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, 3200001, Israel
| | - Ashwin Balakrishnan
- Single Molecule Biophyiscs, Institute of Physical and Theoretical Chemistry, Goethe-University Frankfurt, 60438, Frankfurt, Germany
| | - Mike Heilemann
- Single Molecule Biophyiscs, Institute of Physical and Theoretical Chemistry, Goethe-University Frankfurt, 60438, Frankfurt, Germany
| | - Ricardo Henriques
- Optical cell biology group, Instituto Gulbenkian de Ciência, Oeiras, 2780-156, Portugal
- Optical cell biology group, Gulbenkian Institute of Molecular Medicine, Oeiras, 2780-156, Portugal
- AI-driven Optical Biology, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, 2780-157, Portugal
- UCL Laboratory for Molecular Cell Biology, University College London, London, WC1E 6BT, UK
| | - Yoav Shechtman
- Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, 3200001, Israel
- Department of Mechanical Engineering, University of Texas at Austin, Austin, TX, 78712-1591, USA
- Faculty of Electrical and Computer Engineering, Technion - Israel Institute of Technology, Haifa, 3200001, Israel
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3
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Lascaux P, Hoslett G, Tribble S, Trugenberger C, Antičević I, Otten C, Torrecilla I, Koukouravas S, Zhao Y, Yang H, Aljarbou F, Ruggiano A, Song W, Peron C, Deangeli G, Domingo E, Bancroft J, Carrique L, Johnson E, Vendrell I, Fischer R, Ng AWT, Ngeow J, D'Angiolella V, Raimundo N, Maughan T, Popović M, Milošević I, Ramadan K. TEX264 drives selective autophagy of DNA lesions to promote DNA repair and cell survival. Cell 2024; 187:5698-5718.e26. [PMID: 39265577 DOI: 10.1016/j.cell.2024.08.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 06/04/2024] [Accepted: 08/10/2024] [Indexed: 09/14/2024]
Abstract
DNA repair and autophagy are distinct biological processes vital for cell survival. Although autophagy helps maintain genome stability, there is no evidence of its direct role in the repair of DNA lesions. We discovered that lysosomes process topoisomerase 1 cleavage complexes (TOP1cc) DNA lesions in vertebrates. Selective degradation of TOP1cc by autophagy directs DNA damage repair and cell survival at clinically relevant doses of topoisomerase 1 inhibitors. TOP1cc are exported from the nucleus to lysosomes through a transient alteration of the nuclear envelope and independent of the proteasome. Mechanistically, the autophagy receptor TEX264 acts as a TOP1cc sensor at DNA replication forks, triggering TOP1cc processing by the p97 ATPase and mediating the delivery of TOP1cc to lysosomes in an MRE11-nuclease- and ATR-kinase-dependent manner. We found an evolutionarily conserved role for selective autophagy in DNA repair that enables cell survival, protects genome stability, and is clinically relevant for colorectal cancer patients.
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Affiliation(s)
- Pauline Lascaux
- The MRC Weatherall Institute of Molecular Medicine, Department of Oncology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Gwendoline Hoslett
- The MRC Weatherall Institute of Molecular Medicine, Department of Oncology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Sara Tribble
- The MRC Weatherall Institute of Molecular Medicine, Department of Oncology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Camilla Trugenberger
- The MRC Weatherall Institute of Molecular Medicine, Department of Oncology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Ivan Antičević
- DNA Damage Group, Laboratory for Molecular Ecotoxicology, Department for Marine and Environmental Research, Institute Ruđer Bošković, 10000 Zagreb, Croatia
| | - Cecile Otten
- DNA Damage Group, Laboratory for Molecular Ecotoxicology, Department for Marine and Environmental Research, Institute Ruđer Bošković, 10000 Zagreb, Croatia
| | - Ignacio Torrecilla
- The MRC Weatherall Institute of Molecular Medicine, Department of Oncology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Stelios Koukouravas
- The MRC Weatherall Institute of Molecular Medicine, Department of Oncology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Yichen Zhao
- The MRC Weatherall Institute of Molecular Medicine, Department of Oncology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Hongbin Yang
- The MRC Weatherall Institute of Molecular Medicine, Department of Oncology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Ftoon Aljarbou
- The MRC Weatherall Institute of Molecular Medicine, Department of Oncology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Annamaria Ruggiano
- The MRC Weatherall Institute of Molecular Medicine, Department of Oncology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Wei Song
- The MRC Weatherall Institute of Molecular Medicine, Department of Oncology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Cristiano Peron
- The MRC Weatherall Institute of Molecular Medicine, Department of Oncology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Giulio Deangeli
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 2PY, UK
| | - Enric Domingo
- Department of Oncology, Medical Sciences Division, Old Road Campus Research Building, University of Oxford, Oxford OX3 7DQ, UK
| | - James Bancroft
- Centre for Human Genetics, Nuffield Department of Medicine (NDM), University of Oxford, Oxford OX3 7BN, UK
| | - Loïc Carrique
- Division of Structural Biology, Centre for Human Genetics, Nuffield Department of Medicine (NDM), University of Oxford, Oxford OX3 7BN, UK
| | - Errin Johnson
- Dunn School Bioimaging Facility, Sir William Dunn School of Pathology, University of Oxford, Oxford OX1 3RE, UK
| | - Iolanda Vendrell
- Target Discovery Institute, Nuffield Department of Medicine (NDM), University of Oxford, Oxford OX3 7FZ, UK; Chinese Academy for Medical Sciences Oxford Institute, Nuffield Department of Medicine (NDM), University of Oxford, Oxford OX3 7FZ, UK
| | - Roman Fischer
- Target Discovery Institute, Nuffield Department of Medicine (NDM), University of Oxford, Oxford OX3 7FZ, UK; Chinese Academy for Medical Sciences Oxford Institute, Nuffield Department of Medicine (NDM), University of Oxford, Oxford OX3 7FZ, UK
| | - Alvin Wei Tian Ng
- Lee Kong Chian School of Medicine (LKCMedicine), Nanyang Technological University, Singapore 636921, Singapore
| | - Joanne Ngeow
- Lee Kong Chian School of Medicine (LKCMedicine), Nanyang Technological University, Singapore 636921, Singapore; Cancer Genetics Service, Division of Medical Oncology, National Cancer Centre Singapore, Singapore 169610, Singapore
| | - Vincenzo D'Angiolella
- The MRC Weatherall Institute of Molecular Medicine, Department of Oncology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Edinburgh Cancer Research, CRUK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, EH4 2XU Edinburgh, UK
| | - Nuno Raimundo
- Penn State College of Medicine, Department of Cellular and Molecular Physiology, Hershey, PA 17033, USA; Multidisciplinary Institute for Aging, Center for Innovation in Biomedicine and Biotechnology, University of Coimbra, Coimbra 3000-370, Portugal
| | - Tim Maughan
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7BE, UK
| | - Marta Popović
- DNA Damage Group, Laboratory for Molecular Ecotoxicology, Department for Marine and Environmental Research, Institute Ruđer Bošković, 10000 Zagreb, Croatia
| | - Ira Milošević
- Centre for Human Genetics, Nuffield Department of Medicine (NDM), University of Oxford, Oxford OX3 7BN, UK; Multidisciplinary Institute for Aging, Center for Innovation in Biomedicine and Biotechnology, University of Coimbra, Coimbra 3000-370, Portugal
| | - Kristijan Ramadan
- The MRC Weatherall Institute of Molecular Medicine, Department of Oncology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Lee Kong Chian School of Medicine (LKCMedicine), Nanyang Technological University, Singapore 636921, Singapore.
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4
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Landoni JC, Kleele T, Winter J, Stepp W, Manley S. Mitochondrial Structure, Dynamics, and Physiology: Light Microscopy to Disentangle the Network. Annu Rev Cell Dev Biol 2024; 40:219-240. [PMID: 38976811 DOI: 10.1146/annurev-cellbio-111822-114733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Mitochondria serve as energetic and signaling hubs of the cell: This function results from the complex interplay between their structure, function, dynamics, interactions, and molecular organization. The ability to observe and quantify these properties often represents the puzzle piece critical for deciphering the mechanisms behind mitochondrial function and dysfunction. Fluorescence microscopy addresses this critical need and has become increasingly powerful with the advent of superresolution methods and context-sensitive fluorescent probes. In this review, we delve into advanced light microscopy methods and analyses for studying mitochondrial ultrastructure, dynamics, and physiology, and highlight notable discoveries they enabled.
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Affiliation(s)
- Juan C Landoni
- Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland;
| | - Tatjana Kleele
- Institute of Biochemistry, Swiss Federal Institute of Technology Zürich (ETH), Zürich, Switzerland;
- Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland;
| | - Julius Winter
- Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland;
| | - Willi Stepp
- Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland;
| | - Suliana Manley
- Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland;
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5
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Qu L, Zhao S, Huang Y, Ye X, Wang K, Liu Y, Liu X, Mao H, Hu G, Chen W, Guo C, He J, Tan J, Li H, Chen L, Zhao W. Self-inspired learning for denoising live-cell super-resolution microscopy. Nat Methods 2024; 21:1895-1908. [PMID: 39261639 DOI: 10.1038/s41592-024-02400-9] [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: 01/22/2024] [Accepted: 07/31/2024] [Indexed: 09/13/2024]
Abstract
Every collected photon is precious in live-cell super-resolution (SR) microscopy. Here, we describe a data-efficient, deep learning-based denoising solution to improve diverse SR imaging modalities. The method, SN2N, is a Self-inspired Noise2Noise module with self-supervised data generation and self-constrained learning process. SN2N is fully competitive with supervised learning methods and circumvents the need for large training set and clean ground truth, requiring only a single noisy frame for training. We show that SN2N improves photon efficiency by one-to-two orders of magnitude and is compatible with multiple imaging modalities for volumetric, multicolor, time-lapse SR microscopy. We further integrated SN2N into different SR reconstruction algorithms to effectively mitigate image artifacts. We anticipate SN2N will enable improved live-SR imaging and inspire further advances.
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Affiliation(s)
- Liying Qu
- Innovation Photonics and Imaging Center, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China
| | - Shiqun Zhao
- State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, National Biomedical Imaging Center, School of Future Technology, Peking University, Beijing, China
| | - Yuanyuan Huang
- Innovation Photonics and Imaging Center, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China
| | - Xianxin Ye
- State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, National Biomedical Imaging Center, School of Future Technology, Peking University, Beijing, China
| | - Kunhao Wang
- State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, National Biomedical Imaging Center, School of Future Technology, Peking University, Beijing, China
| | - Yuzhen Liu
- Innovation Photonics and Imaging Center, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China
| | - Xianming Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Heng Mao
- School of Mathematical Sciences, Peking University, Beijing, China
| | - Guangwei Hu
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Wei Chen
- School of Mechanical Science and Engineering, Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan, China
| | - Changliang Guo
- State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, National Biomedical Imaging Center, School of Future Technology, Peking University, Beijing, China
| | - Jiaye He
- National Innovation Center for Advanced Medical Devices, Shenzhen, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jiubin Tan
- Key Laboratory of Ultra-precision Intelligent Instrumentation of Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin, China
| | - Haoyu Li
- Innovation Photonics and Imaging Center, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China
- Key Laboratory of Ultra-precision Intelligent Instrumentation of Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin, China
- Frontiers Science Center for Matter Behave in Space Environment, Harbin Institute of Technology, Harbin, China
- Key Laboratory of Micro-Systems and Micro-Structures Manufacturing of Ministry of Education, Harbin Institute of Technology, Harbin, China
| | - Liangyi Chen
- State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, National Biomedical Imaging Center, School of Future Technology, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
- Beijing Academy of Artificial Intelligence, Beijing, China
| | - Weisong Zhao
- Innovation Photonics and Imaging Center, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China.
- Key Laboratory of Ultra-precision Intelligent Instrumentation of Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin, China.
- Frontiers Science Center for Matter Behave in Space Environment, Harbin Institute of Technology, Harbin, China.
- Key Laboratory of Micro-Systems and Micro-Structures Manufacturing of Ministry of Education, Harbin Institute of Technology, Harbin, China.
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6
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Hockenberry MA, Daugird TA, Legant WR. Cell dynamics revealed by microscopy advances. Curr Opin Cell Biol 2024; 90:102418. [PMID: 39159598 PMCID: PMC11392612 DOI: 10.1016/j.ceb.2024.102418] [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: 05/01/2024] [Revised: 07/23/2024] [Accepted: 07/24/2024] [Indexed: 08/21/2024]
Abstract
Cell biology emerges from spatiotemporally coordinated molecular processes. Recent advances in live-cell microscopy, fueled by a surge in optical, molecular, and computational technologies, have enabled dynamic observations from single molecules to whole organisms. Despite technological leaps, there is still an untapped opportunity to fully leverage their capabilities toward biological insight. We highlight how single-molecule imaging has transformed our understanding of biological processes, with a focus on chromatin organization and transcription in the nucleus. We describe how this was enabled by the close integration of new imaging techniques with analysis tools and discuss the challenges to make a comparable impact at larger scales from organelles to organisms. By highlighting recent successful examples, we describe an outlook of ever-increasing data and the need for seamless integration between dataset visualization and quantification to realize the full potential warranted by advances in new imaging technologies.
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Affiliation(s)
- Max A Hockenberry
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, Chapel Hill, NC, USA
| | - Timothy A Daugird
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Wesley R Legant
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Joint Department of Biomedical Engineering, North Carolina State University, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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7
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Liao Y, Qin C, Zhang X, Ye J, Xu Z, Zong H, Hu N, Zhang D. A dual-mode, image-enhanced, miniaturized microscopy system for incubator-compatible monitoring of live cells. Talanta 2024; 278:126537. [PMID: 38996561 DOI: 10.1016/j.talanta.2024.126537] [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: 04/08/2024] [Revised: 06/26/2024] [Accepted: 07/08/2024] [Indexed: 07/14/2024]
Abstract
Imaging live cells under stable culture conditions is essential to investigate cell physiological activities and proliferation. To achieve this goal, typically, a specialized incubation chamber that creates desired culture conditions needs to be incorporated into a microscopy system to perform cell monitoring. However, such imaging systems are generally large and costly, hampering their wide applications. Recent advances in the field of miniaturized microscopy systems have enabled incubator cell monitoring, providing a hospitable environment for live cells. Although these systems are more cost-effective, they are usually limited in imaging modalities and spatial temporal resolution. Here, we present a dual-mode, image-enhanced, miniaturized microscopy system (termed MiniCube) for direct monitoring of live cells inside incubators. MiniCube enables both bright field imaging and fluorescence imaging with single-cell spatial resolution and sub-second temporal resolution. Moreover, this system can also perform cell monitoring inside the incubator with tunable time scales ranging from a few seconds to days. Meanwhile, automatic cell segmentation and image enhancement are realized by the proposed data analysis pipeline of this system, and the signal-to-noise ratio (SNR) of acquired data is significantly improved using a deep learning based image denoising algorithm. Image data can be acquired with 5 times lower light exposure while maintaining comparable SNR. The versatility of this miniaturized microscopy system lends itself to various applications in biology studies, providing a practical platform and method for studying live cell dynamics within the incubator.
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Affiliation(s)
- Yuheng Liao
- Research Center for Novel Computing Sensing and Intelligent Processing, Zhejiang Laboratory, Hangzhou, 311121, China
| | - Chunlian Qin
- Department of Chemistry, Zhejiang-Israel Joint Laboratory of Self-Assembling Functional Materials, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 310058, China; General Surgery Department, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Children's Health, Hangzhou, 310052, China
| | - Xiaoyu Zhang
- Research Center for Novel Computing Sensing and Intelligent Processing, Zhejiang Laboratory, Hangzhou, 311121, China
| | - Jing Ye
- Research Center for Novel Computing Sensing and Intelligent Processing, Zhejiang Laboratory, Hangzhou, 311121, China
| | - Zhongyuan Xu
- Research Center for Novel Computing Sensing and Intelligent Processing, Zhejiang Laboratory, Hangzhou, 311121, China
| | - Haotian Zong
- Research Center for Novel Computing Sensing and Intelligent Processing, Zhejiang Laboratory, Hangzhou, 311121, China
| | - Ning Hu
- Department of Chemistry, Zhejiang-Israel Joint Laboratory of Self-Assembling Functional Materials, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 310058, China; General Surgery Department, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Children's Health, Hangzhou, 310052, China.
| | - Diming Zhang
- Research Center for Novel Computing Sensing and Intelligent Processing, Zhejiang Laboratory, Hangzhou, 311121, China.
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8
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Roos J, Bancelin S, Delaire T, Wilhelmi A, Levet F, Engelhardt M, Viasnoff V, Galland R, Nägerl UV, Sibarita JB. Arkitekt: streaming analysis and real-time workflows for microscopy. Nat Methods 2024; 21:1884-1894. [PMID: 39294366 DOI: 10.1038/s41592-024-02404-5] [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: 10/12/2023] [Accepted: 08/01/2024] [Indexed: 09/20/2024]
Abstract
Quantitative microscopy workflows have evolved dramatically over the past years, progressively becoming more complex with the emergence of deep learning. Long-standing challenges such as three-dimensional segmentation of complex microscopy data can finally be addressed, and new imaging modalities are breaking records in both resolution and acquisition speed, generating gigabytes if not terabytes of data per day. With this shift in bioimage workflows comes an increasing need for efficient orchestration and data management, necessitating multitool interoperability and the ability to span dedicated computing resources. However, existing solutions are still limited in their flexibility and scalability and are usually restricted to offline analysis. Here we introduce Arkitekt, an open-source middleman between users and bioimage apps that enables complex quantitative microscopy workflows in real time. It allows the orchestration of popular bioimage software locally or remotely in a reliable and efficient manner. It includes visualization and analysis modules, but also mechanisms to execute source code and pilot acquisition software, making 'smart microscopy' a reality.
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Affiliation(s)
- Johannes Roos
- Interdisciplinary Institute for Neuroscience, University of Bordeaux, CNRS, Bordeaux, France
| | - Stéphane Bancelin
- Interdisciplinary Institute for Neuroscience, University of Bordeaux, CNRS, Bordeaux, France
| | - Tom Delaire
- Interdisciplinary Institute for Neuroscience, University of Bordeaux, CNRS, Bordeaux, France
| | | | - Florian Levet
- Interdisciplinary Institute for Neuroscience, University of Bordeaux, CNRS, Bordeaux, France
- Bordeaux Imaging Center, University of Bordeaux, CNRS, INSERM, Bordeaux, France
| | - Maren Engelhardt
- Frankfurt Institute for Advanced Studies, Frankfurt, Germany
- Institute of Anatomy and Cell Biology, Medical Faculty, Johannes Kepler University, Linz, Austria
| | - Virgile Viasnoff
- Mechanobiology Institute, National University of Singapore, Singapore, Singapore
| | - Rémi Galland
- Interdisciplinary Institute for Neuroscience, University of Bordeaux, CNRS, Bordeaux, France
| | - U Valentin Nägerl
- Interdisciplinary Institute for Neuroscience, University of Bordeaux, CNRS, Bordeaux, France
| | - Jean-Baptiste Sibarita
- Interdisciplinary Institute for Neuroscience, University of Bordeaux, CNRS, Bordeaux, France.
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9
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Cao R, Divekar NS, Nuñez JK, Upadhyayula S, Waller L. Neural space-time model for dynamic multi-shot imaging. Nat Methods 2024:10.1038/s41592-024-02417-0. [PMID: 39317729 DOI: 10.1038/s41592-024-02417-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 08/15/2024] [Indexed: 09/26/2024]
Abstract
Computational imaging reconstructions from multiple measurements that are captured sequentially often suffer from motion artifacts if the scene is dynamic. We propose a neural space-time model (NSTM) that jointly estimates the scene and its motion dynamics, without data priors or pre-training. Hence, we can both remove motion artifacts and resolve sample dynamics from the same set of raw measurements used for the conventional reconstruction. We demonstrate NSTM in three computational imaging systems: differential phase-contrast microscopy, three-dimensional structured illumination microscopy and rolling-shutter DiffuserCam. We show that NSTM can recover subcellular motion dynamics and thus reduce the misinterpretation of living systems caused by motion artifacts.
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Affiliation(s)
- Ruiming Cao
- Department of Bioengineering, UC Berkeley, Berkeley, CA, USA.
| | - Nikita S Divekar
- Department of Molecular and Cell Biology, UC Berkeley, Berkeley, CA, USA
| | - James K Nuñez
- Department of Molecular and Cell Biology, UC Berkeley, Berkeley, CA, USA
| | | | - Laura Waller
- Department of Electrical Engineering and Computer Sciences, UC Berkeley, Berkeley, CA, USA.
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10
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Lu Y, Ying Y, Huang C, Li X, Cheng J, Yu R, Ma L, Shuai J, Zhou X, Zhong J. STORM image denoising and information extraction. Biomed Phys Eng Express 2024; 10:065028. [PMID: 39265585 DOI: 10.1088/2057-1976/ad7a02] [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: 02/04/2024] [Accepted: 09/12/2024] [Indexed: 09/14/2024]
Abstract
Stochastic optical reconstruction microscopy (STORM) is extensively utilized in the fields of cell and molecular biology as a super-resolution imaging technique for visualizing cells and molecules. Nonetheless, the imaging process of STORM is frequently susceptible to noise, which can significantly impact the subsequent image analysis. Moreover, there is currently a lack of a comprehensive automated processing approach for analyzing protein aggregation states from a large number of STORM images. This paper initially applies our previously proposed denoising algorithm, UNet-Att, in STORM image denoising. This algorithm was constructed based on attention mechanism and multi-scale features, showcasing a remarkably efficient performance in denoising. Subsequently, we propose a collection of automated image processing algorithms for the ultimate feature extractions and data analyses of the STORM images. The information extraction workflow effectively integrates automated methods of image denoising, objective image segmentation and binarization, and object information extraction, and a novel image information clustering algorithm specifically developed for the morphological analysis of the objects in the STORM images. This automated workflow significantly improves the efficiency of the effective data analysis for large-scale original STORM images.
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Affiliation(s)
- Yuer Lu
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, 325001, People's Republic of China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang, 325001, People's Republic of China
| | - Yongfa Ying
- Department of Physics, Xiamen University, Xiamen, 361102, People's Republic of China
| | - Chengliang Huang
- Academy of Artificial Intelligence, Zhejiang Dongfang Polytechnic, Wenzhou, 325025, People's Republic of China
| | - Xiang Li
- Department of Physics, Xiamen University, Xiamen, 361102, People's Republic of China
| | - Jinyan Cheng
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, 325001, People's Republic of China
| | - Rongwen Yu
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, 325001, People's Republic of China
| | - Lixiang Ma
- Department of Anatomy, Histology & Embryology, School of Basic Medical Sciences, Fudan University, Shanghai, People's Republic of China
| | - Jianwei Shuai
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, 325001, People's Republic of China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang, 325001, People's Republic of China
| | - Xuejin Zhou
- College of Mechanical Engineering and Automation, Huaqiao University, Xiamen, 361021, People's Republic of China
| | - Jinjin Zhong
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, 325001, People's Republic of China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang, 325001, People's Republic of China
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11
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Bertaccini GA, Casanellas I, Evans EL, Nourse JL, Dickinson GD, Liu G, Seal S, Ly AT, Holt JR, Wijerathne TD, Yan S, Hui EE, Lacroix JJ, Panicker MM, Upadhyayula S, Parker I, Pathak MM. Visualizing PIEZO1 Localization and Activity in hiPSC-Derived Single Cells and Organoids with HaloTag Technology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.22.573117. [PMID: 38187535 PMCID: PMC10769387 DOI: 10.1101/2023.12.22.573117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
PIEZO1 is critical to numerous physiological processes, transducing diverse mechanical stimuli into electrical and chemical signals. Recent studies underscore the importance of visualizing endogenous PIEZO1 activity and localization to understand its functional roles. To enable physiologically and clinically relevant studies on human PIEZO1, we genetically engineered human induced pluripotent stem cells (hiPSCs) to express a HaloTag fused to endogenous PIEZO1. Combined with advanced imaging, our chemogenetic platform allows precise visualization of PIEZO1 localization dynamics in various cell types. Furthermore, the PIEZO1-HaloTag hiPSC technology facilitates the non-invasive monitoring of channel activity across diverse cell types using Ca2+-sensitive HaloTag ligands, achieving temporal resolution approaching that of patch clamp electrophysiology. Finally, we used lightsheet imaging of hiPSC-derived neural organoids to achieve molecular scale imaging of PIEZO1 in three-dimensional tissue organoids. Our advances offer a novel platform for studying PIEZO1 mechanotransduction in human cells and tissues, with potential for elucidating disease mechanisms and targeted therapeutic development.
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Affiliation(s)
- Gabriella A Bertaccini
- Department of Physiology and Biophysics, University of California, Irvine, CA, USA
- Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, CA, USA
| | - Ignasi Casanellas
- Department of Physiology and Biophysics, University of California, Irvine, CA, USA
- Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, CA, USA
| | - Elizabeth L Evans
- Department of Physiology and Biophysics, University of California, Irvine, CA, USA
- Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, CA, USA
| | - Jamison L Nourse
- Department of Physiology and Biophysics, University of California, Irvine, CA, USA
- Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, CA, USA
| | - George D Dickinson
- Department of Physiology and Biophysics, University of California, Irvine, CA, USA
| | - Gaoxiang Liu
- Advanced Bioimaging Center, Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | - Sayan Seal
- Advanced Bioimaging Center, Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | - Alan T Ly
- Department of Physiology and Biophysics, University of California, Irvine, CA, USA
- Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, CA, USA
| | - Jesse R Holt
- Department of Physiology and Biophysics, University of California, Irvine, CA, USA
- Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, CA, USA
- Center for Complex Biological Systems, University of California, Irvine, CA, USA
| | - Tharaka D Wijerathne
- Department of Basic Medical Sciences, Western University of Health Sciences, Pomona, CA, USA
| | - Shijun Yan
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
| | - Elliot E Hui
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
| | - Jerome J Lacroix
- Department of Basic Medical Sciences, Western University of Health Sciences, Pomona, CA, USA
| | - Mitradas M Panicker
- Department of Physiology and Biophysics, University of California, Irvine, CA, USA
- Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, CA, USA
| | - Srigokul Upadhyayula
- Advanced Bioimaging Center, Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Ian Parker
- Department of Physiology and Biophysics, University of California, Irvine, CA, USA
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA
| | - Medha M Pathak
- Department of Physiology and Biophysics, University of California, Irvine, CA, USA
- Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, CA, USA
- Center for Complex Biological Systems, University of California, Irvine, CA, USA
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
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12
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James E, Caetano AJ, Sharpe PT. Computational Methods for Image Analysis in Craniofacial Development and Disease. J Dent Res 2024:220345241265048. [PMID: 39272216 DOI: 10.1177/00220345241265048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2024] Open
Abstract
Observation is at the center of all biological sciences. Advances in imaging technologies are therefore essential to derive novel biological insights to better understand the complex workings of living systems. Recent high-throughput sequencing and imaging techniques are allowing researchers to simultaneously address complex molecular variations spatially and temporarily in tissues and organs. The availability of increasingly large dataset sizes has allowed for the evolution of robust deep learning models, designed to interrogate biomedical imaging data. These models are emerging as transformative tools in diagnostic medicine. Combined, these advances allow for dynamic, quantitative, and predictive observations of entire organisms and tissues. Here, we address 3 main tasks of bioimage analysis, image restoration, segmentation, and tracking and discuss new computational tools allowing for 3-dimensional spatial genomics maps. Finally, we demonstrate how these advances have been applied in studies of craniofacial development and oral disease pathogenesis.
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Affiliation(s)
- E James
- Centre for Oral Immunobiology and Regenerative Medicine, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - A J Caetano
- Centre for Oral Immunobiology and Regenerative Medicine, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - P T Sharpe
- Centre for Craniofacial and Regenerative Biology, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK
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13
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Huang X, Wang Q, He J, Ban C, Zheng H, Chen H, Zhu X. Fast Multiphoton Microscopic Imaging Joint Image Super-Resolution for Automated Gleason Grading of Prostate Cancers. JOURNAL OF BIOPHOTONICS 2024:e202400233. [PMID: 39262127 DOI: 10.1002/jbio.202400233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 08/07/2024] [Accepted: 08/08/2024] [Indexed: 09/13/2024]
Abstract
Gleason grading system is dependable for quantifying prostate cancer. This paper introduces a fast multiphoton microscopic imaging method via deep learning for automatic Gleason grading. Due to the contradiction between multiphoton microscopy (MPM) imaging speed and quality, a deep learning architecture (SwinIR) is used for image super-resolution to address this issue. The quality of low-resolution image is improved, which increased the acquisition speed from 7.55 s per frame to 0.24 s per frame. A classification network (Swin Transformer) was introduced for automated Gleason grading. The classification accuracy and Macro-F1 achieved by training on high-resolution images are respectively 90.9% and 90.9%. For training on super-resolution images, the classification accuracy and Macro-F1 are respectively 89.9% and 89.9%. It shows that super-resolution image can provide a comparable performance to high-resolution image. Our results suggested that MPM joint image super-resolution and automatic classification methods hold the potential to be a real-time clinical diagnostic tool for prostate cancer diagnosis.
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Affiliation(s)
- Xinpeng Huang
- Institute of Laser and Optoelectronics Technology, Fujian Provincial Key Laboratory for Photonics Technology, Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, China
| | - Qianqiong Wang
- Institute of Laser and Optoelectronics Technology, Fujian Provincial Key Laboratory for Photonics Technology, Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, China
| | - Jia He
- Institute of Laser and Optoelectronics Technology, Fujian Provincial Key Laboratory for Photonics Technology, Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, China
| | - Chaoran Ban
- Department of Pathology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Hua Zheng
- Institute of Laser and Optoelectronics Technology, Fujian Provincial Key Laboratory for Photonics Technology, Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, China
| | - Hong Chen
- Department of Pathology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Xiaoqin Zhu
- Institute of Laser and Optoelectronics Technology, Fujian Provincial Key Laboratory for Photonics Technology, Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, China
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14
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Schubert MC, Soyka SJ, Tamimi A, Maus E, Schroers J, Wißmann N, Reyhan E, Tetzlaff SK, Yang Y, Denninger R, Peretzke R, Beretta C, Drumm M, Heuer A, Buchert V, Steffens A, Walshon J, McCortney K, Heiland S, Bendszus M, Neher P, Golebiewska A, Wick W, Winkler F, Breckwoldt MO, Kreshuk A, Kuner T, Horbinski C, Kurz FT, Prevedel R, Venkataramani V. Deep intravital brain tumor imaging enabled by tailored three-photon microscopy and analysis. Nat Commun 2024; 15:7383. [PMID: 39256378 PMCID: PMC11387418 DOI: 10.1038/s41467-024-51432-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 08/07/2024] [Indexed: 09/12/2024] Open
Abstract
Intravital 2P-microscopy enables the longitudinal study of brain tumor biology in superficial mouse cortex layers. Intravital microscopy of the white matter, an important route of glioblastoma invasion and recurrence, has not been feasible, due to low signal-to-noise ratios and insufficient spatiotemporal resolution. Here, we present an intravital microscopy and artificial intelligence-based analysis workflow (Deep3P) that enables longitudinal deep imaging of glioblastoma up to a depth of 1.2 mm. We find that perivascular invasion is the preferred invasion route into the corpus callosum and uncover two vascular mechanisms of glioblastoma migration in the white matter. Furthermore, we observe morphological changes after white matter infiltration, a potential basis of an imaging biomarker during early glioblastoma colonization. Taken together, Deep3P allows for a non-invasive intravital investigation of brain tumor biology and its tumor microenvironment at subcortical depths explored, opening up opportunities for studying the neuroscience of brain tumors and other model systems.
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Affiliation(s)
- Marc Cicero Schubert
- Neurology Clinic and National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
- Department of Functional Neuroanatomy, Institute for Anatomy and Cell Biology, Heidelberg University, Heidelberg, Germany
| | - Stella Judith Soyka
- Neurology Clinic and National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
- Department of Functional Neuroanatomy, Institute for Anatomy and Cell Biology, Heidelberg University, Heidelberg, Germany
| | - Amr Tamimi
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Emanuel Maus
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Julian Schroers
- Neurology Clinic and National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
- German Cancer Research Center (DKFZ), Division of Radiology, Heidelberg, Germany
| | - Niklas Wißmann
- Neurology Clinic and National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
- Department of Functional Neuroanatomy, Institute for Anatomy and Cell Biology, Heidelberg University, Heidelberg, Germany
| | - Ekin Reyhan
- Neurology Clinic and National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Svenja Kristin Tetzlaff
- Neurology Clinic and National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
- Department of Functional Neuroanatomy, Institute for Anatomy and Cell Biology, Heidelberg University, Heidelberg, Germany
| | - Yvonne Yang
- Neurology Clinic and National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Robert Denninger
- Neurology Clinic and National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
- Department of Functional Neuroanatomy, Institute for Anatomy and Cell Biology, Heidelberg University, Heidelberg, Germany
| | - Robin Peretzke
- Division of Medical Image Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Carlo Beretta
- Department of Functional Neuroanatomy, Institute for Anatomy and Cell Biology, Heidelberg University, Heidelberg, Germany
| | - Michael Drumm
- Department of Neurological Surgery, Northwestern University, Chicago, IL, USA
| | - Alina Heuer
- Neurology Clinic and National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
| | - Verena Buchert
- Neurology Clinic and National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
| | - Alicia Steffens
- Department of Neurological Surgery, Northwestern University, Chicago, IL, USA
| | - Jordain Walshon
- Department of Neurological Surgery, Northwestern University, Chicago, IL, USA
| | - Kathleen McCortney
- Department of Neurological Surgery, Northwestern University, Chicago, IL, USA
| | - Sabine Heiland
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Peter Neher
- Division of Medical Image Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Anna Golebiewska
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, 1526, Luxembourg, Luxembourg
| | - Wolfgang Wick
- Neurology Clinic and National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Frank Winkler
- Neurology Clinic and National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael O Breckwoldt
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Anna Kreshuk
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Thomas Kuner
- Department of Functional Neuroanatomy, Institute for Anatomy and Cell Biology, Heidelberg University, Heidelberg, Germany
| | - Craig Horbinski
- Department of Neurological Surgery, Northwestern University, Chicago, IL, USA
- Department of Pathology, Northwestern University, Chicago, IL, USA
| | - Felix Tobias Kurz
- German Cancer Research Center (DKFZ), Division of Radiology, Heidelberg, Germany
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
- Division of Neuroradiology, Geneva University Hospitals, Geneva, Switzerland
| | - Robert Prevedel
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
- Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
- Epigenetics and Neurobiology Unit, European Molecular Biology Laboratory, Rome, Italy.
- Molecular Medicine Partnership Unit (MMPU), European Molecular Biology Laboratory, Heidelberg, Germany.
- Interdisciplinary Center of Neurosciences, Heidelberg University, Heidelberg, Germany.
| | - Varun Venkataramani
- Neurology Clinic and National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany.
- Department of Functional Neuroanatomy, Institute for Anatomy and Cell Biology, Heidelberg University, Heidelberg, Germany.
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
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15
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Chen W, Liao M, Bao S, An S, Li W, Liu X, Huang G, Gong H, Luo Q, Xiao C, Li A. A hierarchically annotated dataset drives tangled filament recognition in digital neuron reconstruction. PATTERNS (NEW YORK, N.Y.) 2024; 5:101007. [PMID: 39233689 PMCID: PMC11368685 DOI: 10.1016/j.patter.2024.101007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 04/25/2024] [Accepted: 05/23/2024] [Indexed: 09/06/2024]
Abstract
Reconstructing neuronal morphology is vital for classifying neurons and mapping brain connectivity. However, it remains a significant challenge due to its complex structure, dense distribution, and low image contrast. In particular, AI-assisted methods often yield numerous errors that require extensive manual intervention. Therefore, reconstructing hundreds of neurons is already a daunting task for general research projects. A key issue is the lack of specialized training for challenging regions due to inadequate data and training methods. This study extracted 2,800 challenging neuronal blocks and categorized them into multiple density levels. Furthermore, we enhanced images using an axial continuity-based network that improved three-dimensional voxel resolution while reducing the difficulty of neuron recognition. Comparing the pre- and post-enhancement results in automatic algorithms using fluorescence micro-optical sectioning tomography (fMOST) data, we observed a significant increase in the recall rate. Our study not only enhances the throughput of reconstruction but also provides a fundamental dataset for tangled neuron reconstruction.
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Affiliation(s)
- Wu Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Mingwei Liao
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Shengda Bao
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Sile An
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Wenwei Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Xin Liu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Ganghua Huang
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou 570228, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou 215123, China
| | - Qingming Luo
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou 570228, China
| | - Chi Xiao
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou 570228, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou 215123, China
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou 570228, China
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16
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Wang Z, Wang J, Zhao Y, Jin J, Si W, Chen L, Zhang M, Zhou Y, Mao S, Zheng C, Zhang Y, Chen L, Fei P. 3D live imaging and phenotyping of CAR-T cell mediated-cytotoxicity using high-throughput Bessel oblique plane microscopy. Nat Commun 2024; 15:6677. [PMID: 39107283 PMCID: PMC11303822 DOI: 10.1038/s41467-024-51039-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 07/29/2024] [Indexed: 08/10/2024] Open
Abstract
Clarification of the cytotoxic function of T cells is crucial for understanding human immune responses and immunotherapy procedures. Here, we report a high-throughput Bessel oblique plane microscopy (HBOPM) platform capable of 3D live imaging and phenotyping of chimeric antigen receptor (CAR)-modified T-cell cytotoxicity against cancer cells. The HBOPM platform has the following characteristics: an isotropic subcellular resolution of 320 nm, large-scale scouting over 400 interacting cell pairs, long-term observation across 5 hours, and quantitative analysis of the Terabyte-scale 3D, multichannel, time-lapse image datasets. Using this advanced microscopy platform, several key subcellular events in CAR-T cells are captured and comprehensively analyzed; these events include the instantaneous formation of immune synapses and the sustained changes in the microtubing morphology. Furthermore, we identify the actin retrograde flow speed, the actin depletion coefficient, the microtubule polarization and the contact area of the CAR-T/target cell conjugates as essential parameters strongly correlated with CAR-T-cell cytotoxic function. Our approach will be useful for establishing criteria for quantifying T-cell function in individual patients for all T-cell-based immunotherapies.
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Affiliation(s)
- Zhaofei Wang
- School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Jie Wang
- School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yuxuan Zhao
- School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Jin Jin
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wentian Si
- School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Longbiao Chen
- School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Man Zhang
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yao Zhou
- School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Shiqi Mao
- School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Chunhong Zheng
- International Cancer Institute, Peking University Cancer Hospital and Institute, Peking University, Beijing, China
| | - Yicheng Zhang
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Liting Chen
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Peng Fei
- School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
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17
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Rehman A, Zhovmer A, Sato R, Mukouyama YS, Chen J, Rissone A, Puertollano R, Liu J, Vishwasrao HD, Shroff H, Combs CA, Xue H. Convolutional neural network transformer (CNNT) for fluorescence microscopy image denoising with improved generalization and fast adaptation. Sci Rep 2024; 14:18184. [PMID: 39107416 PMCID: PMC11303381 DOI: 10.1038/s41598-024-68918-2] [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: 05/22/2024] [Accepted: 07/30/2024] [Indexed: 08/10/2024] Open
Abstract
Deep neural networks can improve the quality of fluorescence microscopy images. Previous methods, based on Convolutional Neural Networks (CNNs), require time-consuming training of individual models for each experiment, impairing their applicability and generalization. In this study, we propose a novel imaging-transformer based model, Convolutional Neural Network Transformer (CNNT), that outperforms CNN based networks for image denoising. We train a general CNNT based backbone model from pairwise high-low Signal-to-Noise Ratio (SNR) image volumes, gathered from a single type of fluorescence microscope, an instant Structured Illumination Microscope. Fast adaptation to new microscopes is achieved by fine-tuning the backbone on only 5-10 image volume pairs per new experiment. Results show that the CNNT backbone and fine-tuning scheme significantly reduces training time and improves image quality, outperforming models trained using only CNNs such as 3D-RCAN and Noise2Fast. We show three examples of efficacy of this approach in wide-field, two-photon, and confocal fluorescence microscopy.
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Affiliation(s)
- Azaan Rehman
- Office of AI Research, National Heart, Lung and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, MD, 20892, USA
| | - Alexander Zhovmer
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration (FDA), Silver Spring, MD, 20903, USA
| | - Ryo Sato
- Laboratory of Stem Cell and Neurovascular Research, NHLBI, NIH, Bethesda, MD, 20892, USA
| | - Yoh-Suke Mukouyama
- Laboratory of Stem Cell and Neurovascular Research, NHLBI, NIH, Bethesda, MD, 20892, USA
| | - Jiji Chen
- Advanced Imaging and Microscopy Resource, NIBIB, NIH, Bethesda, MD, 20892, USA
| | - Alberto Rissone
- Laboratory of Protein Trafficking and Organelle Biology, NHLBI, NIH, Bethesda, MD, 20892, USA
| | - Rosa Puertollano
- Laboratory of Protein Trafficking and Organelle Biology, NHLBI, NIH, Bethesda, MD, 20892, USA
| | - Jiamin Liu
- Advanced Imaging and Microscopy Resource, NIBIB, NIH, Bethesda, MD, 20892, USA
| | | | - Hari Shroff
- Janelia Research Campus, Howard Hughes Medical Institute (HHMI), Ashburn, VA, USA
| | - Christian A Combs
- Light Microscopy Core, National Heart, Lung, and Blood Institute, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, 20892, USA.
| | - Hui Xue
- Office of AI Research, National Heart, Lung and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, MD, 20892, USA
- Health Futures, Microsoft Research, Redmond, Washington, 98052, USA
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18
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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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19
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Elmalam N, Ben Nedava L, Zaritsky A. In silico labeling in cell biology: Potential and limitations. Curr Opin Cell Biol 2024; 89:102378. [PMID: 38838549 DOI: 10.1016/j.ceb.2024.102378] [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: 12/17/2023] [Revised: 05/16/2024] [Accepted: 05/16/2024] [Indexed: 06/07/2024]
Abstract
In silico labeling is the computational cross-modality image translation where the output modality is a subcellular marker that is not specifically encoded in the input image, for example, in silico localization of organelles from transmitted light images. In principle, in silico labeling has the potential to facilitate rapid live imaging of multiple organelles with reduced photobleaching and phototoxicity, a technology enabling a major leap toward understanding the cell as an integrated complex system. However, five years have passed since feasibility was attained, without any demonstration of using in silico labeling to uncover new biological insight. In here, we discuss the current state of in silico labeling, the limitations preventing it from becoming a practical tool, and how we can overcome these limitations to reach its full potential.
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Affiliation(s)
- Nitsan Elmalam
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Lion Ben Nedava
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Assaf Zaritsky
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.
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20
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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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21
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Ma C, Tan W, He R, Yan B. Pretraining a foundation model for generalizable fluorescence microscopy-based image restoration. Nat Methods 2024; 21:1558-1567. [PMID: 38609490 DOI: 10.1038/s41592-024-02244-3] [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: 07/27/2023] [Accepted: 03/13/2024] [Indexed: 04/14/2024]
Abstract
Fluorescence microscopy-based image restoration has received widespread attention in the life sciences and has led to significant progress, benefiting from deep learning technology. However, most current task-specific methods have limited generalizability to different fluorescence microscopy-based image restoration problems. Here, we seek to improve generalizability and explore the potential of applying a pretrained foundation model to fluorescence microscopy-based image restoration. We provide a universal fluorescence microscopy-based image restoration (UniFMIR) model to address different restoration problems, and show that UniFMIR offers higher image restoration precision, better generalization and increased versatility. Demonstrations on five tasks and 14 datasets covering a wide range of microscopy imaging modalities and biological samples demonstrate that the pretrained UniFMIR can effectively transfer knowledge to a specific situation via fine-tuning, uncover clear nanoscale biomolecular structures and facilitate high-quality imaging. This work has the potential to inspire and trigger new research highlights for fluorescence microscopy-based image restoration.
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Affiliation(s)
- Chenxi Ma
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China
| | - Weimin Tan
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China
| | - Ruian He
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China
| | - Bo Yan
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China.
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22
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Li Y, Zhang Y, Wang M, Su J, Dong X, Yang Y, Wang H, Li Q. The mammalian actin elongation factor ENAH/MENA contributes to autophagosome formation via its actin regulatory function. Autophagy 2024; 20:1798-1814. [PMID: 38705725 PMCID: PMC11262208 DOI: 10.1080/15548627.2024.2347105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 04/19/2024] [Indexed: 05/07/2024] Open
Abstract
Macroautophagy/autophagy is a catabolic process crucial for degrading cytosolic components and damaged organelles to maintain cellular homeostasis, enabling cells to survive in extreme extracellular environments. ENAH/MENA, a member of the Ena/VASP protein family, functions as a highly efficient actin elongation factor. In this study, our objective was to explore the role of ENAH in the autophagy process. Initially, we demonstrated that depleting ENAH in cancer cells inhibits autophagosome formation. Subsequently, we observed ENAH's colocalization with MAP1LC3/LC3 during tumor cell starvation, dependent on actin cytoskeleton polymerization and the interaction between ENAH and BECN1 (beclin 1). Additionally, mammalian ATG9A formed a ring-like structure around ENAH-LC3 puncta during starvation, relying on actin cytoskeleton polymerization. Furthermore, ENAH's EVH1 and EVH2 domains were found to be indispensable for its colocalization with LC3 and BECN1, while the PRD domain played a crucial role in the formation of the ATG9A ring. Finally, our study revealed ENAH-led actin comet tails in autophagosome trafficking. In conclusion, our findings provide initial insights into the regulatory role of the mammalian actin elongation factor ENAH in autophagy.Abbreviations: 3-MA 3-methyladenine; ABPs actin-binding proteins; ATG autophagy related; ATG9A autophagy related 9A; Baf A1 bafilomycin A1; CM complete medium; CytERM endoplasmic reticulum signal-anchor membrane protein; Cyto D cytochalasin D; EBSS Earl's balanced salt solution; ENAH/MENA ENAH actin regulator; EVH1 Ena/VASP homology 1 domain; EVH2 Ena/VASP homology 2 domain; GAPDH glyceraldehyde-3-phosphate dehydrogenase; Lat B latrunculin B; LC3-I unlipidated form of LC3; LC3-II phosphatidylethanolamine-conjugated form of LC3; MAP1LC3/LC3 microtubule associated protein 1 light chain 3; mEGFP monomeric enhanced green fluorescent protein; mTagBFP2 monomeric Tag blue fluorescent protein 2; OSER organized smooth endoplasmic reticulum; PRD proline-rich domain; PtdIns3K class III phosphatidylinositol 3-kinase; WM wortmannin.
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Affiliation(s)
- Yueheng Li
- Department of Pathology, School of Basic Medical Science, Fudan University, Shanghai, China
- Department of Pharmacology, School of Pharmacy, Fudan University, Shanghai, China
| | - Yafei Zhang
- Department of Pharmacology, School of Pharmacy, Fudan University, Shanghai, China
- Department of Infectious Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui province, China
| | - Menghui Wang
- Department of Pharmacology, School of Pharmacy, Fudan University, Shanghai, China
| | - Junhui Su
- Department of Pharmacology, School of Pharmacy, Fudan University, Shanghai, China
| | - Xinjue Dong
- Department of Pharmacology, School of Pharmacy, Fudan University, Shanghai, China
| | - Yuqi Yang
- Department of Pharmacology, School of Pharmacy, Fudan University, Shanghai, China
| | - Hongshan Wang
- Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, P. R. China
| | - QingQuan Li
- Department of Pharmacology, School of Pharmacy, Fudan University, Shanghai, China
- Department of Infectious Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui province, China
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23
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Lee JR, Boothe T, Mauksch C, Thommen A, Rink JC. Epidermal turnover in the planarian Schmidtea mediterranea involves basal cell extrusion and intestinal digestion. Cell Rep 2024; 43:114305. [PMID: 38906148 DOI: 10.1016/j.celrep.2024.114305] [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: 11/06/2023] [Revised: 03/21/2024] [Accepted: 05/15/2024] [Indexed: 06/23/2024] Open
Abstract
Planarian flatworms undergo continuous internal turnover, wherein old cells are replaced by the division progeny of adult pluripotent stem cells (neoblasts). How cell turnover is carried out at the organismal level remains an intriguing question in planarians and other systems. While previous studies have predominantly focused on neoblast proliferation, little is known about the processes that mediate cell loss during tissue homeostasis. Here, we use the planarian epidermis as a model to study the mechanisms of cell removal. We established a covalent dye-labeling assay and image analysis pipeline to quantify the cell turnover rate in the planarian epidermis. Our findings indicate that the ventral epidermis is highly dynamic and epidermal cells undergo internalization via basal extrusion, followed by a relocation toward the intestine and ultimately digestion by intestinal phagocytes. Overall, our study reveals a complex homeostatic process of cell clearance that may generally allow planarians to catabolize their own cells.
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Affiliation(s)
- Jun-Ru Lee
- Department of Tissue Dynamics and Regeneration, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, 37077 Göttingen, Germany; Graduate Center for Neurosciences, Biophysics, and Molecular Biosciences, University of Göttingen, 37077 Göttingen, Germany
| | - Tobias Boothe
- Department of Tissue Dynamics and Regeneration, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, 37077 Göttingen, Germany
| | - Clemens Mauksch
- Department of Tissue Dynamics and Regeneration, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, 37077 Göttingen, Germany
| | - Albert Thommen
- Cancer Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Jochen C Rink
- Department of Tissue Dynamics and Regeneration, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, 37077 Göttingen, Germany; Faculty of Biology and Psychology, Georg-August-University, Göttingen, Germany.
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24
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Mim MS, Kumar N, Levis M, Unger MF, Miranda G, Gazzo D, Robinett T, Zartman JJ. Piezo regulates epithelial topology and promotes precision in organ size control. Cell Rep 2024; 43:114398. [PMID: 38935502 DOI: 10.1016/j.celrep.2024.114398] [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: 09/21/2023] [Revised: 05/09/2024] [Accepted: 06/10/2024] [Indexed: 06/29/2024] Open
Abstract
Mechanosensitive Piezo channels regulate cell division, cell extrusion, and cell death. However, systems-level functions of Piezo in regulating organogenesis remain poorly understood. Here, we demonstrate that Piezo controls epithelial cell topology to ensure precise organ growth by integrating live-imaging experiments with pharmacological and genetic perturbations and computational modeling. Notably, the knockout or knockdown of Piezo increases bilateral asymmetry in wing size. Piezo's multifaceted functions can be deconstructed as either autonomous or non-autonomous based on a comparison between tissue-compartment-level perturbations or between genetic perturbation populations at the whole-tissue level. A computational model that posits cell proliferation and apoptosis regulation through modulation of the cutoff tension required for Piezo channel activation explains key cell and tissue phenotypes arising from perturbations of Piezo expression levels. Our findings demonstrate that Piezo promotes robustness in regulating epithelial topology and is necessary for precise organ size control.
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Affiliation(s)
- Mayesha Sahir Mim
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA; Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Nilay Kumar
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Megan Levis
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA; Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Maria F Unger
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Gabriel Miranda
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - David Gazzo
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA; Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Trent Robinett
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Jeremiah J Zartman
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA; Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN 46556, USA; Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA.
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25
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Sui L, Dahmann C. A cellular tilting mechanism important for dynamic tissue shape changes and cell differentiation in Drosophila. Dev Cell 2024; 59:1794-1808.e5. [PMID: 38692272 DOI: 10.1016/j.devcel.2024.04.011] [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: 12/20/2022] [Revised: 12/15/2023] [Accepted: 04/11/2024] [Indexed: 05/03/2024]
Abstract
Dynamic changes in three-dimensional cell shape are important for tissue form and function. In the developing Drosophila eye, photoreceptor differentiation requires the progression across the tissue of an epithelial fold known as the morphogenetic furrow. Morphogenetic furrow progression involves apical cell constriction and movement of apical cell edges. Here, we show that cells progressing through the morphogenetic furrow move their basal edges in opposite direction to their apical edges, resulting in a cellular tilting movement. We further demonstrate that cells generate, at their basal side, oriented, force-generating protrusions. Knockdown of the protein kinase Src42A or photoactivation of a dominant-negative form of the small GTPase Rac1 reduces protrusion formation. Impaired protrusion formation stalls basal cell movement and slows down morphogenetic furrow progression and photoreceptor differentiation. This work identifies a cellular tilting mechanism important for the generation of dynamic tissue shape changes and cell differentiation.
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Affiliation(s)
- Liyuan Sui
- School of Science, Technische Universität Dresden, 01062 Dresden, Germany
| | - Christian Dahmann
- School of Science, Technische Universität Dresden, 01062 Dresden, Germany; Cluster of Excellence Physics of Life, Technische Universität Dresden, 01062 Dresden, Germany.
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26
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Guo M, Wu Y, Hobson CM, Su Y, Qian S, Krueger E, Christensen R, Kroeschell G, Bui J, Chaw M, Zhang L, Liu J, Hou X, Han X, Lu Z, Ma X, Zhovmer A, Combs C, Moyle M, Yemini E, Liu H, Liu Z, Benedetto A, La Riviere P, Colón-Ramos D, Shroff H. Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.15.562439. [PMID: 37986950 PMCID: PMC10659418 DOI: 10.1101/2023.10.15.562439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics into the imaging path. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained 'de-aberration' networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos.
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27
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Rueda-Alaña E, Grillo M, Vázquez E, Salas SM, Senovilla-Ganzo R, Escobar L, Quintas A, Benguría A, Aransay AM, Bengoa-Vergniory N, Dopazo A, Encinas JM, Nilsson M, García-Moreno F. BirthSeq, a new method to isolate and analyze dated cells in different vertebrates. Development 2024; 151:dev202429. [PMID: 38856078 DOI: 10.1242/dev.202429] [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: 10/19/2023] [Accepted: 05/27/2024] [Indexed: 06/11/2024]
Abstract
Embryonic development is a complex and dynamic process that unfolds over time and involves the production and diversification of increasing numbers of cells. The impact of developmental time on the formation of the central nervous system is well documented, with evidence showing that time plays a crucial role in establishing the identity of neuronal subtypes. However, the study of how time translates into genetic instructions driving cell fate is limited by the scarcity of suitable experimental tools. We introduce BirthSeq, a new method for isolating and analyzing cells based on their birth date. This innovative technique allows for in vivo labeling of cells, isolation via fluorescence-activated cell sorting, and analysis using high-throughput techniques. We calibrated the BirthSeq method for developmental organs across three vertebrate species (mouse, chick and gecko), and utilized it for single-cell RNA sequencing and novel spatially resolved transcriptomic approaches in mouse and chick, respectively. Overall, BirthSeq provides a versatile tool for studying virtually any tissue in different vertebrate organisms, aiding developmental biology research by targeting cells and their temporal cues.
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Affiliation(s)
- Eneritz Rueda-Alaña
- Achucarro Basque Center for Neuroscience, Scientific Park of the University of the Basque Country (UPV/EHU), 48940, Leioa, Spain
- Department of Neuroscience, Faculty of Medicine and Odontology, UPV/EHU, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain
| | - Marco Grillo
- Science for Life Laboratory, Department of Biophysics and Biochemistry, Stockholm University, 17165, Solna, Sweden
| | - Enrique Vázquez
- Genomics Unit, Centro Nacional de Investigaciones Cardiovasculares (CNIC), 28029 Madrid, Spain
| | - Sergio Marco Salas
- Science for Life Laboratory, Department of Biophysics and Biochemistry, Stockholm University, 17165, Solna, Sweden
| | - Rodrigo Senovilla-Ganzo
- Achucarro Basque Center for Neuroscience, Scientific Park of the University of the Basque Country (UPV/EHU), 48940, Leioa, Spain
- Department of Neuroscience, Faculty of Medicine and Odontology, UPV/EHU, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain
| | - Laura Escobar
- Achucarro Basque Center for Neuroscience, Scientific Park of the University of the Basque Country (UPV/EHU), 48940, Leioa, Spain
| | - Ana Quintas
- Genomics Unit, Centro Nacional de Investigaciones Cardiovasculares (CNIC), 28029 Madrid, Spain
| | - Alberto Benguría
- Genomics Unit, Centro Nacional de Investigaciones Cardiovasculares (CNIC), 28029 Madrid, Spain
| | - Ana María Aransay
- Genome Analysis Platform, Center for Cooperative Research in Biosciences (CIC bioGUNE), Basque Research and Technology Alliance (BRTA), Bizkaia Technology Park, 48160 Derio, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), 28029 Madrid, Spain
| | - Nora Bengoa-Vergniory
- Achucarro Basque Center for Neuroscience, Scientific Park of the University of the Basque Country (UPV/EHU), 48940, Leioa, Spain
- Department of Neuroscience, Faculty of Medicine and Odontology, UPV/EHU, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain
- Oxford Parkinson's Disease Centre and Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, OX1 3PT, UK
- IKERBASQUE Foundation, María Díaz de Haro 3, 6th Floor, 48013 BilbaoSpain
| | - Ana Dopazo
- Genomics Unit, Centro Nacional de Investigaciones Cardiovasculares (CNIC), 28029 Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), 28029 Madrid, Spain
| | - Juan Manuel Encinas
- Achucarro Basque Center for Neuroscience, Scientific Park of the University of the Basque Country (UPV/EHU), 48940, Leioa, Spain
- Department of Neuroscience, Faculty of Medicine and Odontology, UPV/EHU, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain
- IKERBASQUE Foundation, María Díaz de Haro 3, 6th Floor, 48013 BilbaoSpain
| | - Mats Nilsson
- Science for Life Laboratory, Department of Biophysics and Biochemistry, Stockholm University, 17165, Solna, Sweden
| | - Fernando García-Moreno
- Achucarro Basque Center for Neuroscience, Scientific Park of the University of the Basque Country (UPV/EHU), 48940, Leioa, Spain
- Department of Neuroscience, Faculty of Medicine and Odontology, UPV/EHU, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain
- IKERBASQUE Foundation, María Díaz de Haro 3, 6th Floor, 48013 BilbaoSpain
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28
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Ertürk A. Deep 3D histology powered by tissue clearing, omics and AI. Nat Methods 2024; 21:1153-1165. [PMID: 38997593 DOI: 10.1038/s41592-024-02327-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 05/28/2024] [Indexed: 07/14/2024]
Abstract
To comprehensively understand tissue and organism physiology and pathophysiology, it is essential to create complete three-dimensional (3D) cellular maps. These maps require structural data, such as the 3D configuration and positioning of tissues and cells, and molecular data on the constitution of each cell, spanning from the DNA sequence to protein expression. While single-cell transcriptomics is illuminating the cellular and molecular diversity across species and tissues, the 3D spatial context of these molecular data is often overlooked. Here, I discuss emerging 3D tissue histology techniques that add the missing third spatial dimension to biomedical research. Through innovations in tissue-clearing chemistry, labeling and volumetric imaging that enhance 3D reconstructions and their synergy with molecular techniques, these technologies will provide detailed blueprints of entire organs or organisms at the cellular level. Machine learning, especially deep learning, will be essential for extracting meaningful insights from the vast data. Further development of integrated structural, molecular and computational methods will unlock the full potential of next-generation 3D histology.
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Affiliation(s)
- Ali Ertürk
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Zentrum München, Neuherberg, Germany.
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University, Munich, Germany.
- School of Medicine, Koç University, İstanbul, Turkey.
- Deep Piction GmbH, Munich, Germany.
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29
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Iyer RR, Applegate CC, Arogundade OH, Bangru S, Berg IC, Emon B, Porras-Gomez M, Hsieh PH, Jeong Y, Kim Y, Knox HJ, Moghaddam AO, Renteria CA, Richard C, Santaliz-Casiano A, Sengupta S, Wang J, Zambuto SG, Zeballos MA, Pool M, Bhargava R, Gaskins HR. Inspiring a convergent engineering approach to measure and model the tissue microenvironment. Heliyon 2024; 10:e32546. [PMID: 38975228 PMCID: PMC11226808 DOI: 10.1016/j.heliyon.2024.e32546] [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: 02/16/2024] [Revised: 05/22/2024] [Accepted: 06/05/2024] [Indexed: 07/09/2024] Open
Abstract
Understanding the molecular and physical complexity of the tissue microenvironment (TiME) in the context of its spatiotemporal organization has remained an enduring challenge. Recent advances in engineering and data science are now promising the ability to study the structure, functions, and dynamics of the TiME in unprecedented detail; however, many advances still occur in silos that rarely integrate information to study the TiME in its full detail. This review provides an integrative overview of the engineering principles underlying chemical, optical, electrical, mechanical, and computational science to probe, sense, model, and fabricate the TiME. In individual sections, we first summarize the underlying principles, capabilities, and scope of emerging technologies, the breakthrough discoveries enabled by each technology and recent, promising innovations. We provide perspectives on the potential of these advances in answering critical questions about the TiME and its role in various disease and developmental processes. Finally, we present an integrative view that appreciates the major scientific and educational aspects in the study of the TiME.
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Affiliation(s)
- Rishyashring R. Iyer
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Catherine C. Applegate
- Division of Nutritional Sciences, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Opeyemi H. Arogundade
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Sushant Bangru
- Department of Biochemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Ian C. Berg
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Bashar Emon
- Department of Mechanical Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Marilyn Porras-Gomez
- Department of Materials Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Pei-Hsuan Hsieh
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Yoon Jeong
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Yongdeok Kim
- Department of Materials Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Hailey J. Knox
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Amir Ostadi Moghaddam
- Department of Mechanical Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Carlos A. Renteria
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Craig Richard
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Ashlie Santaliz-Casiano
- Division of Nutritional Sciences, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Sourya Sengupta
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Jason Wang
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Samantha G. Zambuto
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Maria A. Zeballos
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Marcia Pool
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Rohit Bhargava
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Mechanical Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Chemical and Biochemical Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- NIH/NIBIB P41 Center for Label-free Imaging and Multiscale Biophotonics (CLIMB), University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - H. Rex Gaskins
- Division of Nutritional Sciences, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Animal Sciences, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Biomedical and Translational Sciences, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Pathobiology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
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30
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Meirovitch Y, Park CF, Mi L, Potocek P, Sawmya S, Li Y, Chandok IS, Athey TL, Karlupia N, Wu Y, Berger DR, Schalek R, Pfister H, Schoenmakers R, Peemen M, Lichtman JW, Samuel ADT, Shavit N. SmartEM: machine-learning guided electron microscopy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.05.561103. [PMID: 38915594 PMCID: PMC11195061 DOI: 10.1101/2023.10.05.561103] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Connectomics provides essential nanometer-resolution, synapse-level maps of neural circuits to understand brain activity and behavior. However, few researchers have access to the high-throughput electron microscopes necessary to generate enough data for whole circuit or brain reconstruction. To date, machine-learning methods have been used after the collection of images by electron microscopy (EM) to accelerate and improve neuronal segmentation, synapse reconstruction and other data analysis. With the computational improvements in processing EM images, acquiring EM images has now become the rate-limiting step. Here, in order to speed up EM imaging, we integrate machine-learning into real-time image acquisition in a singlebeam scanning electron microscope. This SmartEM approach allows an electron microscope to perform intelligent, data-aware imaging of specimens. SmartEM allocates the proper imaging time for each region of interest - scanning all pixels equally rapidly, then re-scanning small subareas more slowly where a higher quality signal is required to achieve accurate segmentability, in significantly less time. We demonstrate that this pipeline achieves a 7-fold acceleration of image acquisition time for connectomics using a commercial single-beam SEM. We apply SmartEM to reconstruct a portion of mouse cortex with the same accuracy as traditional microscopy but in less time.
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Affiliation(s)
- Yaron Meirovitch
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Physics, Harvard University, Cambridge, MA 02138, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Core Francisco Park
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Physics, Harvard University, Cambridge, MA 02138, USA
| | - Lu Mi
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Pavel Potocek
- Thermo Fisher Scientific, Eindhoven, the Netherlands
- Saarland University, 66123, Saarbrücken, Germany
| | - Shashata Sawmya
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Yicong Li
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA
| | - Ishaan Singh Chandok
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Physics, Harvard University, Cambridge, MA 02138, USA
| | - Thomas L Athey
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Neha Karlupia
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Yuelong Wu
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Daniel R Berger
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Richard Schalek
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Hanspeter Pfister
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA
| | | | | | - Jeff W Lichtman
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Aravinthan D T Samuel
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Physics, Harvard University, Cambridge, MA 02138, USA
| | - Nir Shavit
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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31
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Cao Y, Xu B, Li B, Fu H. Advanced Design of Soft Robots with Artificial Intelligence. NANO-MICRO LETTERS 2024; 16:214. [PMID: 38869734 PMCID: PMC11176285 DOI: 10.1007/s40820-024-01423-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 04/22/2024] [Indexed: 06/14/2024]
Abstract
A comprehensive review focused on the whole systems of the soft robotics with artificial intelligence, which can feel, think, react and interact with humans, is presented. The design strategies concerning about various aspects of the soft robotics, like component materials, device structures, prepared technologies, integrated method, and potential applications, are summarized. A broad outlook on the future considerations for the soft robots is proposed. In recent years, breakthrough has been made in the field of artificial intelligence (AI), which has also revolutionized the industry of robotics. Soft robots featured with high-level safety, less weight, lower power consumption have always been one of the research hotspots. Recently, multifunctional sensors for perception of soft robotics have been rapidly developed, while more algorithms and models of machine learning with high accuracy have been optimized and proposed. Designs of soft robots with AI have also been advanced ranging from multimodal sensing, human–machine interaction to effective actuation in robotic systems. Nonetheless, comprehensive reviews concerning the new developments and strategies for the ingenious design of the soft robotic systems equipped with AI are rare. Here, the new development is systematically reviewed in the field of soft robots with AI. First, background and mechanisms of soft robotic systems are briefed, after which development focused on how to endow the soft robots with AI, including the aspects of feeling, thought and reaction, is illustrated. Next, applications of soft robots with AI are systematically summarized and discussed together with advanced strategies proposed for performance enhancement. Design thoughts for future intelligent soft robotics are pointed out. Finally, some perspectives are put forward.
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Affiliation(s)
- Ying Cao
- Nanotechnology Center, School of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong, 999077, People's Republic of China
| | - Bingang Xu
- Nanotechnology Center, School of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong, 999077, People's Republic of China.
| | - Bin Li
- Bioinspired Engineering and Biomechanics Center, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China
| | - Hong Fu
- Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong, 999077, People's Republic of China.
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32
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Garcia SB, Schlotter AP, Pereira D, Polleux F, Hammond LA. RESPAN: an accurate, unbiased and automated pipeline for analysis of dendritic morphology and dendritic spine mapping. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.06.597812. [PMID: 38895232 PMCID: PMC11185717 DOI: 10.1101/2024.06.06.597812] [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
Accurate and unbiased reconstructions of neuronal morphology, including quantification of dendritic spine morphology and distribution, are widely used in neuroscience but remain a major roadblock for large-scale analysis. Traditionally, spine analysis has required labor-intensive manual annotation, which is prone to human error and impractical for large 3D datasets. Previous automated tools for reconstructing neuronal morphology and quantitative dendritic spine analysis face challenges in generating accurate results and, following close inspection, often require extensive manual correction. While recent tools leveraging deep learning approaches have substantially increased accuracy, they lack functionality and useful outputs, necessitating additional tools to perform a complete analysis and limiting their utility. In this paper, we describe Restoration Enhanced SPine And Neuron (RESPAN) analysis, a new comprehensive pipeline developed as an open-source, easily deployable solution that harnesses recent advances in deep learning and GPU processing. Our approach demonstrates high accuracy and robustness, validated extensively across a range of imaging modalities for automated dendrite and spine mapping. It also offers extensive visual and tabulated data outputs, including detailed morphological and spatial metrics, dendritic spine classification, and 3D renderings. Additionally, RESPAN includes tools for validating results, ensuring scientific rigor and reproducibility.
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Affiliation(s)
- Sergio B. Garcia
- Department of Neuroscience, Columbia University, New York, NY, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Alexa P. Schlotter
- Department of Neuroscience, Columbia University, New York, NY, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Daniela Pereira
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon 1400-038, Portugal
| | - Franck Polleux
- Department of Neuroscience, Columbia University, New York, NY, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Luke A. Hammond
- Department of Neuroscience, Columbia University, New York, NY, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
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33
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Opatovski N, Nehme E, Zoref N, Barzilai I, Orange Kedem R, Ferdman B, Keselman P, Alalouf O, Shechtman Y. Depth-enhanced high-throughput microscopy by compact PSF engineering. Nat Commun 2024; 15:4861. [PMID: 38849376 PMCID: PMC11161645 DOI: 10.1038/s41467-024-48502-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 05/03/2024] [Indexed: 06/09/2024] Open
Abstract
High-throughput microscopy is vital for screening applications, where three-dimensional (3D) cellular models play a key role. However, due to defocus susceptibility, current 3D high-throughput microscopes require axial scanning, which lowers throughput and increases photobleaching and photodamage. Point spread function (PSF) engineering is an optical method that enables various 3D imaging capabilities, yet it has not been implemented in high-throughput microscopy due to the cumbersome optical extension it typically requires. Here we demonstrate compact PSF engineering in the objective lens, which allows us to enhance the imaging depth of field and, combined with deep learning, recover 3D information using single snapshots. Beyond the applications shown here, this work showcases the usefulness of high-throughput microscopy in obtaining training data for deep learning-based algorithms, applicable to a variety of microscopy modalities.
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Affiliation(s)
- Nadav Opatovski
- Russell Berrie Nanotechnology Institute, Technion - Israel Institute of Technology, Haifa, Israel
| | - Elias Nehme
- Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
- Department of Electrical and Computer Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Noam Zoref
- Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Ilana Barzilai
- Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Reut Orange Kedem
- Russell Berrie Nanotechnology Institute, Technion - Israel Institute of Technology, Haifa, Israel
| | - Boris Ferdman
- Russell Berrie Nanotechnology Institute, Technion - Israel Institute of Technology, Haifa, Israel
| | - Paul Keselman
- Sartorius Stedim North America Inc., Bohemia, NY, USA
| | - Onit Alalouf
- Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Yoav Shechtman
- Russell Berrie Nanotechnology Institute, Technion - Israel Institute of Technology, Haifa, Israel.
- Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel.
- Department of Mechanical Engineering, University of Texas at Austin, Austin, TX, USA.
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34
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Choi YK, Feng L, Jeong WK, Kim J. Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity. Brain Inform 2024; 11:15. [PMID: 38833195 DOI: 10.1186/s40708-024-00228-9] [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: 03/29/2024] [Accepted: 05/08/2024] [Indexed: 06/06/2024] Open
Abstract
Mapping neural connections within the brain has been a fundamental goal in neuroscience to understand better its functions and changes that follow aging and diseases. Developments in imaging technology, such as microscopy and labeling tools, have allowed researchers to visualize this connectivity through high-resolution brain-wide imaging. With this, image processing and analysis have become more crucial. However, despite the wealth of neural images generated, access to an integrated image processing and analysis pipeline to process these data is challenging due to scattered information on available tools and methods. To map the neural connections, registration to atlases and feature extraction through segmentation and signal detection are necessary. In this review, our goal is to provide an updated overview of recent advances in these image-processing methods, with a particular focus on fluorescent images of the mouse brain. Our goal is to outline a pathway toward an integrated image-processing pipeline tailored for connecto-informatics. An integrated workflow of these image processing will facilitate researchers' approach to mapping brain connectivity to better understand complex brain networks and their underlying brain functions. By highlighting the image-processing tools available for fluroscent imaging of the mouse brain, this review will contribute to a deeper grasp of connecto-informatics, paving the way for better comprehension of brain connectivity and its implications.
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Affiliation(s)
- Yoon Kyoung Choi
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, South Korea
- Department of Computer Science and Engineering, Korea University, Seoul, South Korea
| | | | - Won-Ki Jeong
- Department of Computer Science and Engineering, Korea University, Seoul, South Korea
| | - Jinhyun Kim
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, South Korea.
- Department of Computer Science and Engineering, Korea University, Seoul, South Korea.
- KIST-SKKU Brain Research Center, SKKU Institute for Convergence, Sungkyunkwan University, Suwon, South Korea.
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35
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Rumberger JL, Greenwald NF, Ranek JS, Boonrat P, Walker C, Franzen J, Varra SR, Kong A, Sowers C, Liu CC, Averbukh I, Piyadasa H, Vanguri R, Nederlof I, Wang XJ, Van Valen D, Kok M, Hollmann TJ, Kainmueller D, Angelo M. Automated classification of cellular expression in multiplexed imaging data with Nimbus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.02.597062. [PMID: 38895405 PMCID: PMC11185540 DOI: 10.1101/2024.06.02.597062] [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
Multiplexed imaging offers a powerful approach to characterize the spatial topography of tissues in both health and disease. To analyze such data, the specific combination of markers that are present in each cell must be enumerated to enable accurate phenotyping, a process that often relies on unsupervised clustering. We constructed the Pan-Multiplex (Pan-M) dataset containing 197 million distinct annotations of marker expression across 15 different cell types. We used Pan-M to create Nimbus, a deep learning model to predict marker positivity from multiplexed image data. Nimbus is a pre-trained model that uses the underlying images to classify marker expression across distinct cell types, from different tissues, acquired using different microscope platforms, without requiring any retraining. We demonstrate that Nimbus predictions capture the underlying staining patterns of the full diversity of markers present in Pan-M. We then show how Nimbus predictions can be integrated with downstream clustering algorithms to robustly identify cell subtypes in image data. We have open-sourced Nimbus and Pan-M to enable community use at https://github.com/angelolab/Nimbus-Inference.
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Affiliation(s)
- J. Lorenz Rumberger
- Max-Delbruck-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Humboldt-Universität zu Berlin, Faculty of Mathematics and Natural Sciences, Berlin, Germany
- Helmholtz Imaging
| | - Noah F. Greenwald
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Jolene S. Ranek
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Potchara Boonrat
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Cameron Walker
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Jannik Franzen
- Max-Delbruck-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Helmholtz Imaging
- Charité University Medicine, Berlin, Germany
| | | | - Alex Kong
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Cameron Sowers
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Candace C. Liu
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Inna Averbukh
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Hadeesha Piyadasa
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Rami Vanguri
- Division of Precision Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, New York, USA
| | - Iris Nederlof
- Division of Tumor Biology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Xuefei Julie Wang
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA, USA
| | - David Van Valen
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Marleen Kok
- Division of Tumor Biology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Travis J. Hollmann
- Division of Precision Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, New York, USA
| | - Dagmar Kainmueller
- Max-Delbruck-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Helmholtz Imaging
- Potsdam University, Digital Engineering Faculty, Germany
| | - Michael Angelo
- Department of Pathology, Stanford University, Stanford, California, USA
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36
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Jerome AD, Sas AR, Wang Y, Hammond LA, Wen J, Atkinson JR, Webb A, Liu T, Segal BM. Cytokine polarized, alternatively activated bone marrow neutrophils drive axon regeneration. Nat Immunol 2024; 25:957-968. [PMID: 38811815 DOI: 10.1038/s41590-024-01836-7] [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: 10/25/2023] [Accepted: 04/11/2024] [Indexed: 05/31/2024]
Abstract
The adult central nervous system (CNS) possesses a limited capacity for self-repair. Severed CNS axons typically fail to regrow. There is an unmet need for treatments designed to enhance neuronal viability, facilitate axon regeneration and ultimately restore lost neurological functions to individuals affected by traumatic CNS injury, multiple sclerosis, stroke and other neurological disorders. Here we demonstrate that both mouse and human bone marrow neutrophils, when polarized with a combination of recombinant interleukin-4 (IL-4) and granulocyte colony-stimulating factor (G-CSF), upregulate alternative activation markers and produce an array of growth factors, thereby gaining the capacity to promote neurite outgrowth. Moreover, adoptive transfer of IL-4/G-CSF-polarized bone marrow neutrophils into experimental models of CNS injury triggered substantial axon regeneration within the optic nerve and spinal cord. These findings have far-reaching implications for the future development of autologous myeloid cell-based therapies that may bring us closer to effective solutions for reversing CNS damage.
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Affiliation(s)
- Andrew D Jerome
- Department of Neurology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
- The Neuroscience Research Institute, The Ohio State University, Columbus, OH, USA
| | - Andrew R Sas
- Department of Neurology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
- The Neuroscience Research Institute, The Ohio State University, Columbus, OH, USA
| | - Yan Wang
- Department of Neurology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
- The Neuroscience Research Institute, The Ohio State University, Columbus, OH, USA
| | - Luke A Hammond
- Department of Neurology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
- The Neuroscience Research Institute, The Ohio State University, Columbus, OH, USA
| | - Jing Wen
- The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
| | - Jeffrey R Atkinson
- Department of Neurology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Amy Webb
- The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - Tom Liu
- Department of Neurology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
- The Neuroscience Research Institute, The Ohio State University, Columbus, OH, USA
| | - Benjamin M Segal
- Department of Neurology, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
- The Neuroscience Research Institute, The Ohio State University, Columbus, OH, USA.
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Aghigh A, Jargot G, Zaouter C, Preston SEJ, Mohammadi MS, Ibrahim H, Del Rincón SV, Patten K, Légaré F. A comparative study of CARE 2D and N2V 2D for tissue-specific denoising in second harmonic generation imaging. JOURNAL OF BIOPHOTONICS 2024; 17:e202300565. [PMID: 38566461 DOI: 10.1002/jbio.202300565] [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: 12/27/2023] [Revised: 03/11/2024] [Accepted: 03/17/2024] [Indexed: 04/04/2024]
Abstract
This study explored the application of deep learning in second harmonic generation (SHG) microscopy, a rapidly growing area. This study focuses on the impact of glycerol concentration on image noise in SHG microscopy and compares two image restoration techniques: Noise-to-Void 2D (N2V 2D, no reference image restoration) and content-aware image restoration (CARE 2D, full reference image restoration). We demonstrated that N2V 2D effectively restored the images affected by high glycerol concentrations. To reduce sample exposure and damage, this study further addresses low-power SHG imaging by reducing the laser power by 70% using deep learning techniques. CARE 2D excels in preserving detailed structures, whereas N2V 2D maintains natural muscle structure. This study highlights the strengths and limitations of these models in specific SHG microscopy applications, offering valuable insights and potential advancements in the field .
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Affiliation(s)
- Arash Aghigh
- Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, Varennes, Québec, Canada
| | - Gaëtan Jargot
- Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, Varennes, Québec, Canada
| | - Charlotte Zaouter
- Armand-Frappier Santé Biotechnologie Research Centre, Laval, Québec, Canada
| | - Samuel E J Preston
- Department of Experimental Medicine, Faculty of Medicine, McGill University, Montréal, Québec, Canada
- Gerald Bronfman Department of Oncology, Segal Cancer Centre, Lady Davis Institute and Jewish General Hospital, McGill University, Montréal, Québec, Canada
| | - Melika Saadat Mohammadi
- Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, Varennes, Québec, Canada
| | - Heide Ibrahim
- Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, Varennes, Québec, Canada
| | - Sonia V Del Rincón
- Department of Experimental Medicine, Faculty of Medicine, McGill University, Montréal, Québec, Canada
- Gerald Bronfman Department of Oncology, Segal Cancer Centre, Lady Davis Institute and Jewish General Hospital, McGill University, Montréal, Québec, Canada
| | - Kessen Patten
- Armand-Frappier Santé Biotechnologie Research Centre, Laval, Québec, Canada
| | - François Légaré
- Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, Varennes, Québec, Canada
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Shroff H, Testa I, Jug F, Manley S. Live-cell imaging powered by computation. Nat Rev Mol Cell Biol 2024; 25:443-463. [PMID: 38378991 DOI: 10.1038/s41580-024-00702-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/10/2024] [Indexed: 02/22/2024]
Abstract
The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preserving cell viability. Computational methods can help to address this challenge and are now shifting the boundaries of what is possible to capture in living systems. In this Review, we discuss these computational methods focusing on artificial intelligence-based approaches that can be layered on top of commonly used existing microscopies as well as hybrid methods that integrate computation and microscope hardware. We specifically discuss how computational approaches can improve the signal-to-noise ratio, spatial resolution, temporal resolution and multi-colour capacity of live-cell imaging.
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Affiliation(s)
- Hari Shroff
- Janelia Research Campus, Howard Hughes Medical Institute (HHMI), Ashburn, VA, USA
| | - Ilaria Testa
- Department of Applied Physics and Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Florian Jug
- Fondazione Human Technopole (HT), Milan, Italy
| | - Suliana Manley
- Institute of Physics, School of Basic Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland.
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Lu C, Chen K, Qiu H, Chen X, Chen G, Qi X, Jiang H. Diffusion-based deep learning method for augmenting ultrastructural imaging and volume electron microscopy. Nat Commun 2024; 15:4677. [PMID: 38824146 PMCID: PMC11144272 DOI: 10.1038/s41467-024-49125-z] [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: 08/18/2023] [Accepted: 05/20/2024] [Indexed: 06/03/2024] Open
Abstract
Electron microscopy (EM) revolutionized the way to visualize cellular ultrastructure. Volume EM (vEM) has further broadened its three-dimensional nanoscale imaging capacity. However, intrinsic trade-offs between imaging speed and quality of EM restrict the attainable imaging area and volume. Isotropic imaging with vEM for large biological volumes remains unachievable. Here, we developed EMDiffuse, a suite of algorithms designed to enhance EM and vEM capabilities, leveraging the cutting-edge image generation diffusion model. EMDiffuse generates realistic predictions with high resolution ultrastructural details and exhibits robust transferability by taking only one pair of images of 3 megapixels to fine-tune in denoising and super-resolution tasks. EMDiffuse also demonstrated proficiency in the isotropic vEM reconstruction task, generating isotropic volume even in the absence of isotropic training data. We demonstrated the robustness of EMDiffuse by generating isotropic volumes from seven public datasets obtained from different vEM techniques and instruments. The generated isotropic volume enables accurate three-dimensional nanoscale ultrastructure analysis. EMDiffuse also features self-assessment functionalities on predictions' reliability. We envision EMDiffuse to pave the way for investigations of the intricate subcellular nanoscale ultrastructure within large volumes of biological systems.
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Affiliation(s)
- Chixiang Lu
- Department of Chemistry, The University of Hong Kong, Hong Kong, China
| | - Kai Chen
- Department of Chemistry, The University of Hong Kong, Hong Kong, China
- School of Molecular Sciences, The University of Western Australia, Perth, WA, Australia
| | - Heng Qiu
- Department of Chemistry, The University of Hong Kong, Hong Kong, China
| | - Xiaojun Chen
- School of Molecular Sciences, The University of Western Australia, Perth, WA, Australia
| | - Gu Chen
- Department of Chemistry, The University of Hong Kong, Hong Kong, China
| | - Xiaojuan Qi
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China.
| | - Haibo Jiang
- Department of Chemistry, The University of Hong Kong, Hong Kong, China.
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Jiang C, Gedeon A, Lyu Y, Landgraf E, Zhang Y, Hou X, Kondepudi A, Chowdury A, Lee H, Hollon T. Super-resolution of biomedical volumes with 2D supervision. CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS. IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION. WORKSHOPS 2024; 2024:6966-6977. [PMID: 39355755 PMCID: PMC11444667 DOI: 10.1109/cvprw63382.2024.00690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2024]
Abstract
Volumetric biomedical microscopy has the potential to increase the diagnostic information extracted from clinical tissue specimens and improve the diagnostic accuracy of both human pathologists and computational pathology models. Unfortunately, barriers to integrating 3-dimensional (3D) volumetric microscopy into clinical medicine include long imaging times, poor depth/z-axis resolution, and an insufficient amount of high-quality volumetric data. Leveraging the abundance of high-resolution 2D microscopy data, we introduce masked slice diffusion for super-resolution (MSDSR), which exploits the inherent equivalence in the data-generating distribution across all spatial dimensions of biological specimens. This intrinsic characteristic allows for super-resolution models trained on high-resolution images from one plane (e.g., XY) to effectively generalize to others (XZ, YZ), overcoming the traditional dependency on orientation. We focus on the application of MSDSR to stimulated Raman histology (SRH), an optical imaging modality for biological specimen analysis and intraoperative diagnosis, characterized by its rapid acquisition of high-resolution 2D images but slow and costly optical z-sectioning. To evaluate MSDSR's efficacy, we introduce a new performance metric, SliceFID, and demonstrate MSDSR's superior performance over baseline models through extensive evaluations. Our findings reveal that MSDSR not only significantly enhances the quality and resolution of 3D volumetric data, but also addresses major obstacles hindering the broader application of 3D volumetric microscopy in clinical diagnostics and biomedical research.
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41
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Chen H, Yan G, Wen MH, Brooks KN, Zhang Y, Huang PS, Chen TY. Advancements and Practical Considerations for Biophysical Research: Navigating the Challenges and Future of Super-resolution Microscopy. CHEMICAL & BIOMEDICAL IMAGING 2024; 2:331-344. [PMID: 38817319 PMCID: PMC11134610 DOI: 10.1021/cbmi.4c00019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Revised: 04/06/2024] [Accepted: 04/10/2024] [Indexed: 06/01/2024]
Abstract
The introduction of super-resolution microscopy (SRM) has significantly advanced our understanding of cellular and molecular dynamics, offering a detailed view previously beyond our reach. Implementing SRM in biophysical research, however, presents numerous challenges. This review addresses the crucial aspects of utilizing SRM effectively, from selecting appropriate fluorophores and preparing samples to analyzing complex data sets. We explore recent technological advancements and methodological improvements that enhance the capabilities of SRM. Emphasizing the integration of SRM with other analytical methods, we aim to overcome inherent limitations and expand the scope of biological insights achievable. By providing a comprehensive guide for choosing the most suitable SRM methods based on specific research objectives, we aim to empower researchers to explore complex biological processes with enhanced precision and clarity, thereby advancing the frontiers of biophysical research.
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Affiliation(s)
- Huanhuan Chen
- Department of Chemistry, University of Houston, Houston, Texas 77204, United States
| | - Guangjie Yan
- Department of Chemistry, University of Houston, Houston, Texas 77204, United States
| | - Meng-Hsuan Wen
- Department of Chemistry, University of Houston, Houston, Texas 77204, United States
| | - Kameron N. Brooks
- Department of Chemistry, University of Houston, Houston, Texas 77204, United States
| | - Yuteng Zhang
- Department of Chemistry, University of Houston, Houston, Texas 77204, United States
| | - Pei-San Huang
- Department of Chemistry, University of Houston, Houston, Texas 77204, United States
| | - Tai-Yen Chen
- Department of Chemistry, University of Houston, Houston, Texas 77204, United States
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Qiao C, Zeng Y, Meng Q, Chen X, Chen H, Jiang T, Wei R, Guo J, Fu W, Lu H, Li D, Wang Y, Qiao H, Wu J, Li D, Dai Q. Zero-shot learning enables instant denoising and super-resolution in optical fluorescence microscopy. Nat Commun 2024; 15:4180. [PMID: 38755148 PMCID: PMC11099110 DOI: 10.1038/s41467-024-48575-9] [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: 10/07/2023] [Accepted: 05/07/2024] [Indexed: 05/18/2024] Open
Abstract
Computational super-resolution methods, including conventional analytical algorithms and deep learning models, have substantially improved optical microscopy. Among them, supervised deep neural networks have demonstrated outstanding performance, however, demanding abundant high-quality training data, which are laborious and even impractical to acquire due to the high dynamics of living cells. Here, we develop zero-shot deconvolution networks (ZS-DeconvNet) that instantly enhance the resolution of microscope images by more than 1.5-fold over the diffraction limit with 10-fold lower fluorescence than ordinary super-resolution imaging conditions, in an unsupervised manner without the need for either ground truths or additional data acquisition. We demonstrate the versatile applicability of ZS-DeconvNet on multiple imaging modalities, including total internal reflection fluorescence microscopy, three-dimensional wide-field microscopy, confocal microscopy, two-photon microscopy, lattice light-sheet microscopy, and multimodal structured illumination microscopy, which enables multi-color, long-term, super-resolution 2D/3D imaging of subcellular bioprocesses from mitotic single cells to multicellular embryos of mouse and C. elegans.
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Affiliation(s)
- Chang Qiao
- Department of Automation, Tsinghua University, 100084, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China
- Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography, Tsinghua University, 100084, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, 100010, Beijing, China
| | - Yunmin Zeng
- Department of Automation, Tsinghua University, 100084, Beijing, China
| | - Quan Meng
- National Laboratory of Biomacromolecules, New Cornerstone Science Laboratory, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101, Beijing, China
- College of Life Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Xingye Chen
- Department of Automation, Tsinghua University, 100084, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China
- Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography, Tsinghua University, 100084, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, 100010, Beijing, China
- Research Institute for Frontier Science, Beihang University, 100191, Beijing, China
| | - Haoyu Chen
- National Laboratory of Biomacromolecules, New Cornerstone Science Laboratory, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101, Beijing, China
- College of Life Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Tao Jiang
- National Laboratory of Biomacromolecules, New Cornerstone Science Laboratory, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101, Beijing, China
- College of Life Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Rongfei Wei
- National Laboratory of Biomacromolecules, New Cornerstone Science Laboratory, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101, Beijing, China
| | - Jiabao Guo
- National Laboratory of Biomacromolecules, New Cornerstone Science Laboratory, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101, Beijing, China
- College of Life Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Wenfeng Fu
- National Laboratory of Biomacromolecules, New Cornerstone Science Laboratory, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101, Beijing, China
- College of Life Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Huaide Lu
- National Laboratory of Biomacromolecules, New Cornerstone Science Laboratory, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101, Beijing, China
- College of Life Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Di Li
- National Laboratory of Biomacromolecules, New Cornerstone Science Laboratory, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101, Beijing, China
| | - Yuwang Wang
- Beijing National Research Center for Information Science and Technology, Tsinghua University, 100084, Beijing, China
| | - Hui Qiao
- Department of Automation, Tsinghua University, 100084, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China
- Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography, Tsinghua University, 100084, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, 100010, Beijing, China
| | - Jiamin Wu
- Department of Automation, Tsinghua University, 100084, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China
- Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography, Tsinghua University, 100084, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, 100010, Beijing, China
| | - Dong Li
- National Laboratory of Biomacromolecules, New Cornerstone Science Laboratory, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101, Beijing, China.
- College of Life Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China.
| | - Qionghai Dai
- Department of Automation, Tsinghua University, 100084, Beijing, China.
- Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China.
- Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography, Tsinghua University, 100084, Beijing, China.
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, 100010, Beijing, China.
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Kuznetsova V, Coogan Á, Botov D, Gromova Y, Ushakova EV, Gun'ko YK. Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2308912. [PMID: 38241607 PMCID: PMC11167410 DOI: 10.1002/adma.202308912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 01/10/2024] [Indexed: 01/21/2024]
Abstract
Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.
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Affiliation(s)
- Vera Kuznetsova
- School of Chemistry, CRANN and AMBER Research Centres, Trinity College Dublin, College Green, Dublin, D02 PN40, Ireland
| | - Áine Coogan
- School of Chemistry, CRANN and AMBER Research Centres, Trinity College Dublin, College Green, Dublin, D02 PN40, Ireland
| | - Dmitry Botov
- Everypixel Media Innovation Group, 021 Fillmore St., PMB 15, San Francisco, CA, 94115, USA
- Neapolis University Pafos, 2 Danais Avenue, Pafos, 8042, Cyprus
| | - Yulia Gromova
- Department of Molecular and Cellular Biology, Harvard University, 52 Oxford St., Cambridge, MA, 02138, USA
| | - Elena V Ushakova
- Department of Materials Science and Engineering, and Centre for Functional Photonics (CFP), City University of Hong Kong, Hong Kong SAR, 999077, P. R. China
| | - Yurii K Gun'ko
- School of Chemistry, CRANN and AMBER Research Centres, Trinity College Dublin, College Green, Dublin, D02 PN40, Ireland
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Müller A, Schmidt D, Albrecht JP, Rieckert L, Otto M, Galicia Garcia LE, Fabig G, Solimena M, Weigert M. Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets. Nat Protoc 2024; 19:1436-1466. [PMID: 38424188 DOI: 10.1038/s41596-024-00957-5] [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: 03/07/2023] [Accepted: 11/24/2023] [Indexed: 03/02/2024]
Abstract
Volume electron microscopy is the method of choice for the in situ interrogation of cellular ultrastructure at the nanometer scale, and with the increase in large raw image datasets generated, improving computational strategies for image segmentation and spatial analysis is necessary. Here we describe a practical and annotation-efficient pipeline for organelle-specific segmentation, spatial analysis and visualization of large volume electron microscopy datasets using freely available, user-friendly software tools that can be run on a single standard workstation. The procedures are aimed at researchers in the life sciences with modest computational expertise, who use volume electron microscopy and need to generate three-dimensional (3D) segmentation labels for different types of cell organelles while minimizing manual annotation efforts, to analyze the spatial interactions between organelle instances and to visualize the 3D segmentation results. We provide detailed guidelines for choosing well-suited segmentation tools for specific cell organelles, and to bridge compatibility issues between freely available open-source tools, we distribute the critical steps as easily installable Album solutions for deep learning segmentation, spatial analysis and 3D rendering. Our detailed description can serve as a reference for similar projects requiring particular strategies for single- or multiple-organelle analysis, which can be achieved with computational resources commonly available to single-user setups.
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Affiliation(s)
- Andreas Müller
- Molecular Diabetology, University Hospital and Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany.
- Paul Langerhans Institute Dresden (PLID) of the Helmholtz Center Munich at the University Hospital Carl Gustav Carus and Faculty of Medicine of the TU Dresden, Dresden, Germany.
- German Center for Diabetes Research, Neuherberg, Germany.
| | - Deborah Schmidt
- HELMHOLTZ IMAGING, Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany.
| | - Jan Philipp Albrecht
- HELMHOLTZ IMAGING, Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany
- Humboldt-Universität zu Berlin, Faculty of Mathematics and Natural Sciences, Berlin, Germany
| | - Lucas Rieckert
- HELMHOLTZ IMAGING, Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany
| | - Maximilian Otto
- HELMHOLTZ IMAGING, Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany
| | - Leticia Elizabeth Galicia Garcia
- Molecular Diabetology, University Hospital and Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
- Paul Langerhans Institute Dresden (PLID) of the Helmholtz Center Munich at the University Hospital Carl Gustav Carus and Faculty of Medicine of the TU Dresden, Dresden, Germany
- German Center for Diabetes Research, Neuherberg, Germany
- DFG Cluster of Excellence 'Physics of Life', TU Dresden, Dresden, Germany
| | - Gunar Fabig
- Experimental Center, Faculty of Medicine Carl Gustav Carus, Dresden, Dresden, Germany
| | - Michele Solimena
- Molecular Diabetology, University Hospital and Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
- Paul Langerhans Institute Dresden (PLID) of the Helmholtz Center Munich at the University Hospital Carl Gustav Carus and Faculty of Medicine of the TU Dresden, Dresden, Germany
- German Center for Diabetes Research, Neuherberg, Germany
- DFG Cluster of Excellence 'Physics of Life', TU Dresden, Dresden, Germany
| | - Martin Weigert
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
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45
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Yang K, Zhang H, Qiu Y, Zhai T, Zhang Z. Self-Supervised Joint Learning for pCLE Image Denoising. SENSORS (BASEL, SWITZERLAND) 2024; 24:2853. [PMID: 38732957 PMCID: PMC11086271 DOI: 10.3390/s24092853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 04/26/2024] [Accepted: 04/28/2024] [Indexed: 05/13/2024]
Abstract
Probe-based confocal laser endoscopy (pCLE) has emerged as a powerful tool for disease diagnosis, yet it faces challenges such as the formation of hexagonal patterns in images due to the inherent characteristics of fiber bundles. Recent advancements in deep learning offer promise in image denoising, but the acquisition of clean-noisy image pairs for training networks across all potential scenarios can be prohibitively costly. Few studies have explored training denoising networks on such pairs. Here, we propose an innovative self-supervised denoising method. Our approach integrates noise prediction networks, image quality assessment networks, and denoising networks in a collaborative, jointly trained manner. Compared to prior self-supervised denoising methods, our approach yields superior results on pCLE images and fluorescence microscopy images. In summary, our novel self-supervised denoising technique enhances image quality in pCLE diagnosis by leveraging the synergy of noise prediction, image quality assessment, and denoising networks, surpassing previous methods on both pCLE and fluorescence microscopy images.
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Affiliation(s)
| | - Haojie Zhang
- State Key Lab of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China; (K.Y.); (Y.Q.); (T.Z.); (Z.Z.)
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46
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Ibrahim KA, Naidu AS, Miljkovic H, Radenovic A, Yang W. Label-Free Techniques for Probing Biomolecular Condensates. ACS NANO 2024; 18:10738-10757. [PMID: 38609349 DOI: 10.1021/acsnano.4c01534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/14/2024]
Abstract
Biomolecular condensates play important roles in a wide array of fundamental biological processes, such as cellular compartmentalization, cellular regulation, and other biochemical reactions. Since their discovery and first observations, an extensive and expansive library of tools has been developed to investigate various aspects and properties, encompassing structural and compositional information, material properties, and their evolution throughout the life cycle from formation to eventual dissolution. This Review presents an overview of the expanded set of tools and methods that researchers use to probe the properties of biomolecular condensates across diverse scales of length, concentration, stiffness, and time. In particular, we review recent years' exciting development of label-free techniques and methodologies. We broadly organize the set of tools into 3 categories: (1) imaging-based techniques, such as transmitted-light microscopy (TLM) and Brillouin microscopy (BM), (2) force spectroscopy techniques, such as atomic force microscopy (AFM) and the optical tweezer (OT), and (3) microfluidic platforms and emerging technologies. We point out the tools' key opportunities, challenges, and future perspectives and analyze their correlative potential as well as compatibility with other techniques. Additionally, we review emerging techniques, namely, differential dynamic microscopy (DDM) and interferometric scattering microscopy (iSCAT), that have huge potential for future applications in studying biomolecular condensates. Finally, we highlight how some of these techniques can be translated for diagnostics and therapy purposes. We hope this Review serves as a useful guide for new researchers in this field and aids in advancing the development of new biophysical tools to study biomolecular condensates.
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47
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Husser MC, Pham NP, Law C, Araujo FRB, Martin VJJ, Piekny A. Endogenous tagging using split mNeonGreen in human iPSCs for live imaging studies. eLife 2024; 12:RP92819. [PMID: 38652106 PMCID: PMC11037917 DOI: 10.7554/elife.92819] [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: 04/25/2024] Open
Abstract
Endogenous tags have become invaluable tools to visualize and study native proteins in live cells. However, generating human cell lines carrying endogenous tags is difficult due to the low efficiency of homology-directed repair. Recently, an engineered split mNeonGreen protein was used to generate a large-scale endogenous tag library in HEK293 cells. Using split mNeonGreen for large-scale endogenous tagging in human iPSCs would open the door to studying protein function in healthy cells and across differentiated cell types. We engineered an iPS cell line to express the large fragment of the split mNeonGreen protein (mNG21-10) and showed that it enables fast and efficient endogenous tagging of proteins with the short fragment (mNG211). We also demonstrate that neural network-based image restoration enables live imaging studies of highly dynamic cellular processes such as cytokinesis in iPSCs. This work represents the first step towards a genome-wide endogenous tag library in human stem cells.
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Affiliation(s)
| | - Nhat P Pham
- Biology Department, Concordia University, Montreal, Canada
| | - Chris Law
- Biology Department, Concordia University, Montreal, Canada
- Center for Microscopy and Cellular Imaging, Concordia University, Montreal, Canada
| | - Flavia R B Araujo
- Center for Applied Synthetic Biology, Concordia University, Montreal, Canada
| | - Vincent J J Martin
- Biology Department, Concordia University, Montreal, Canada
- Center for Applied Synthetic Biology, Concordia University, Montreal, Canada
| | - Alisa Piekny
- Biology Department, Concordia University, Montreal, Canada
- Center for Microscopy and Cellular Imaging, Concordia University, Montreal, Canada
- Center for Applied Synthetic Biology, Concordia University, Montreal, Canada
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Albanese M, Chen HR, Gapp M, Muenchhoff M, Yang HH, Peterhoff D, Hoffmann K, Xiao Q, Ruhle A, Ambiel I, Schneider S, Mejías-Pérez E, Stern M, Wratil PR, Hofmann K, Amann L, Jocham L, Fuchs T, Ulivi AF, Besson-Girard S, Weidlich S, Schneider J, Spinner CD, Sutter K, Dittmer U, Humpe A, Baumeister P, Wieser A, Rothenfusser S, Bogner J, Roider J, Knolle P, Hengel H, Wagner R, Laketa V, Fackler OT, Keppler OT. Receptor transfer between immune cells by autoantibody-enhanced, CD32-driven trogocytosis is hijacked by HIV-1 to infect resting CD4 T cells. Cell Rep Med 2024; 5:101483. [PMID: 38579727 PMCID: PMC11031382 DOI: 10.1016/j.xcrm.2024.101483] [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: 07/21/2023] [Revised: 12/23/2023] [Accepted: 03/01/2024] [Indexed: 04/07/2024]
Abstract
Immune cell phenotyping frequently detects lineage-unrelated receptors. Here, we report that surface receptors can be transferred from primary macrophages to CD4 T cells and identify the Fcγ receptor CD32 as driver and cargo of this trogocytotic transfer. Filamentous CD32+ nanoprotrusions deposit distinct plasma membrane patches onto target T cells. Transferred receptors confer cell migration and adhesion properties, and macrophage-derived membrane patches render resting CD4 T cells susceptible to infection by serving as hotspots for HIV-1 binding. Antibodies that recognize T cell epitopes enhance CD32-mediated trogocytosis. Such autoreactive anti-HIV-1 envelope antibodies can be found in the blood of HIV-1 patients and, consistently, the percentage of CD32+ CD4 T cells is increased in their blood. This CD32-mediated, antigen-independent cell communication mode transiently expands the receptor repertoire and functionality of immune cells. HIV-1 hijacks this mechanism by triggering the generation of trogocytosis-promoting autoantibodies to gain access to immune cells critical to its persistence.
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Affiliation(s)
- Manuel Albanese
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, Faculty of Medicine, LMU München, Munich, Germany; Department for Clinical Sciences and Community Health (DISCCO), University of Milan, Milan, Italy
| | - Hong-Ru Chen
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, Faculty of Medicine, LMU München, Munich, Germany.
| | - Madeleine Gapp
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, Faculty of Medicine, LMU München, Munich, Germany
| | - Maximilian Muenchhoff
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, Faculty of Medicine, LMU München, Munich, Germany; German Centre for Infection Research (DZIF), Partner Site Munich, Munich, Germany
| | - Hsiu-Hui Yang
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, Faculty of Medicine, LMU München, Munich, Germany
| | - David Peterhoff
- Institute of Medical Microbiology and Hygiene, Molecular Microbiology (Virology), University of Regensburg, Regensburg, Germany
| | - Katja Hoffmann
- Institute of Virology, University Medical Center, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Qianhao Xiao
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, Faculty of Medicine, LMU München, Munich, Germany
| | - Adrian Ruhle
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, Faculty of Medicine, LMU München, Munich, Germany
| | - Ina Ambiel
- Department of Infectious Diseases, Heidelberg University, Medical Faculty Heidelberg, Integrative Virology, Center for Integrative Infectious Disease Research (CIID), Heidelberg, Germany; German Centre for Infection Research (DZIF), Partner Site Heidelberg, Heidelberg, Germany
| | - Stephanie Schneider
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, Faculty of Medicine, LMU München, Munich, Germany
| | - Ernesto Mejías-Pérez
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, Faculty of Medicine, LMU München, Munich, Germany
| | - Marcel Stern
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, Faculty of Medicine, LMU München, Munich, Germany
| | - Paul R Wratil
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, Faculty of Medicine, LMU München, Munich, Germany
| | - Katharina Hofmann
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, Faculty of Medicine, LMU München, Munich, Germany
| | - Laura Amann
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, Faculty of Medicine, LMU München, Munich, Germany
| | - Linda Jocham
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, Faculty of Medicine, LMU München, Munich, Germany
| | - Thimo Fuchs
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, Faculty of Medicine, LMU München, Munich, Germany
| | | | - Simon Besson-Girard
- Institute for Stroke and Dementia Research, University Hospital, LMU München, Munich, Germany
| | - Simon Weidlich
- Technical University of Munich, School of Medicine, University Hospital Rechts der Isar, Department of Internal Medicine II, Munich, Germany
| | - Jochen Schneider
- Technical University of Munich, School of Medicine, University Hospital Rechts der Isar, Department of Internal Medicine II, Munich, Germany
| | - Christoph D Spinner
- German Centre for Infection Research (DZIF), Partner Site Munich, Munich, Germany; Technical University of Munich, School of Medicine, University Hospital Rechts der Isar, Department of Internal Medicine II, Munich, Germany
| | - Kathrin Sutter
- University Hospital Essen, University Duisburg-Essen, Institute for Virology and Institute for Translational HIV Research, Essen, Germany
| | - Ulf Dittmer
- University Hospital Essen, University Duisburg-Essen, Institute for Virology and Institute for Translational HIV Research, Essen, Germany
| | - Andreas Humpe
- Department of Transfusion Medicine, Cell Therapeutics, and Hemostaseology, Department of Anesthesiology, University Hospital Munich, Munich, Germany
| | - Philipp Baumeister
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital, LMU München, Munich, Germany
| | - Andreas Wieser
- German Centre for Infection Research (DZIF), Partner Site Munich, Munich, Germany; Max von Pettenkofer Institute, Medical Microbiology and Hospital Epidemiology, Faculty of Medicine, LMU München, Munich, Germany; Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU München, Munich, Germany
| | - Simon Rothenfusser
- Division of Clinical Pharmacology, University Hospital, LMU München and Unit Clinical Pharmacology (EKliP), Helmholtz Center for Environmental Health, Munich, Germany
| | - Johannes Bogner
- German Centre for Infection Research (DZIF), Partner Site Munich, Munich, Germany; Division of Infectious Diseases, University Hospital, Medizinische Klinik und Poliklinik IV, LMU München, Munich, Germany
| | - Julia Roider
- German Centre for Infection Research (DZIF), Partner Site Munich, Munich, Germany; Division of Infectious Diseases, University Hospital, Medizinische Klinik und Poliklinik IV, LMU München, Munich, Germany
| | - Percy Knolle
- German Centre for Infection Research (DZIF), Partner Site Munich, Munich, Germany; Institute of Molecular Immunology and Experimental Oncology, School of Medicine, Technical University of Munich (TUM), Munich, Germany
| | - Hartmut Hengel
- Institute of Virology, University Medical Center, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Ralf Wagner
- Institute of Medical Microbiology and Hygiene, Molecular Microbiology (Virology), University of Regensburg, Regensburg, Germany
| | - Vibor Laketa
- German Centre for Infection Research (DZIF), Partner Site Heidelberg, Heidelberg, Germany; Department of Infectious Diseases, Heidelberg University, Medical Faculty Heidelberg, Virology, Center for Integrative Infectious Disease Research (CIID), Heidelberg, Germany
| | - Oliver T Fackler
- Department of Infectious Diseases, Heidelberg University, Medical Faculty Heidelberg, Integrative Virology, Center for Integrative Infectious Disease Research (CIID), Heidelberg, Germany; German Centre for Infection Research (DZIF), Partner Site Heidelberg, Heidelberg, Germany.
| | - Oliver T Keppler
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, Faculty of Medicine, LMU München, Munich, Germany; German Centre for Infection Research (DZIF), Partner Site Munich, Munich, Germany.
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49
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Kukanja P, Langseth CM, Rubio Rodríguez-Kirby LA, Agirre E, Zheng C, Raman A, Yokota C, Avenel C, Tiklová K, Guerreiro-Cacais AO, Olsson T, Hilscher MM, Nilsson M, Castelo-Branco G. Cellular architecture of evolving neuroinflammatory lesions and multiple sclerosis pathology. Cell 2024; 187:1990-2009.e19. [PMID: 38513664 DOI: 10.1016/j.cell.2024.02.030] [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: 06/18/2023] [Revised: 12/13/2023] [Accepted: 02/23/2024] [Indexed: 03/23/2024]
Abstract
Multiple sclerosis (MS) is a neurological disease characterized by multifocal lesions and smoldering pathology. Although single-cell analyses provided insights into cytopathology, evolving cellular processes underlying MS remain poorly understood. We investigated the cellular dynamics of MS by modeling temporal and regional rates of disease progression in mouse experimental autoimmune encephalomyelitis (EAE). By performing single-cell spatial expression profiling using in situ sequencing (ISS), we annotated disease neighborhoods and found centrifugal evolution of active lesions. We demonstrated that disease-associated (DA)-glia arise independently of lesions and are dynamically induced and resolved over the disease course. Single-cell spatial mapping of human archival MS spinal cords confirmed the differential distribution of homeostatic and DA-glia, enabled deconvolution of active and inactive lesions into sub-compartments, and identified new lesion areas. By establishing a spatial resource of mouse and human MS neuropathology at a single-cell resolution, our study unveils the intricate cellular dynamics underlying MS.
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Affiliation(s)
- Petra Kukanja
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, 17177 Stockholm, Sweden.
| | - Christoffer M Langseth
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, 17154 Stockholm, Sweden.
| | - Leslie A Rubio Rodríguez-Kirby
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Eneritz Agirre
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Chao Zheng
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Amitha Raman
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, 17154 Stockholm, Sweden
| | - Chika Yokota
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, 17154 Stockholm, Sweden
| | - Christophe Avenel
- Department of Information Technology, Uppsala University, 752 37 Uppsala, Sweden; BioImage Informatics Facility, Science for Life Laboratory, SciLifeLab, 751 05 Uppsala, Sweden
| | - Katarina Tiklová
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, 17154 Stockholm, Sweden
| | - André O Guerreiro-Cacais
- Center for Molecular Medicine, Department of Clinical Neuroscience, Karolinska Institutet, Karolinska University Hospital, 171 76 Solna, Sweden
| | - Tomas Olsson
- Center for Molecular Medicine, Department of Clinical Neuroscience, Karolinska Institutet, Karolinska University Hospital, 171 76 Solna, Sweden
| | - Markus M Hilscher
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, 17154 Stockholm, Sweden
| | - Mats Nilsson
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, 17154 Stockholm, Sweden.
| | - Gonçalo Castelo-Branco
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, 17177 Stockholm, Sweden.
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50
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Dai W, Wong IHM, Wong TTW. Exceeding the limit for microscopic image translation with a deep learning-based unified framework. PNAS NEXUS 2024; 3:pgae133. [PMID: 38601859 PMCID: PMC11004937 DOI: 10.1093/pnasnexus/pgae133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 03/19/2024] [Indexed: 04/12/2024]
Abstract
Deep learning algorithms have been widely used in microscopic image translation. The corresponding data-driven models can be trained by supervised or unsupervised learning depending on the availability of paired data. However, general cases are where the data are only roughly paired such that supervised learning could be invalid due to data unalignment, and unsupervised learning would be less ideal as the roughly paired information is not utilized. In this work, we propose a unified framework (U-Frame) that unifies supervised and unsupervised learning by introducing a tolerance size that can be adjusted automatically according to the degree of data misalignment. Together with the implementation of a global sampling rule, we demonstrate that U-Frame consistently outperforms both supervised and unsupervised learning in all levels of data misalignments (even for perfectly aligned image pairs) in a myriad of image translation applications, including pseudo-optical sectioning, virtual histological staining (with clinical evaluations for cancer diagnosis), improvement of signal-to-noise ratio or resolution, and prediction of fluorescent labels, potentially serving as new standard for image translation.
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Affiliation(s)
- Weixing Dai
- Department of Chemical and Biological Engineering, Translational and Advanced Bioimaging Laboratory, Hong Kong University of Science and Technology, Hong Kong 999077, China
| | - Ivy H M Wong
- Department of Chemical and Biological Engineering, Translational and Advanced Bioimaging Laboratory, Hong Kong University of Science and Technology, Hong Kong 999077, China
| | - Terence T W Wong
- Department of Chemical and Biological Engineering, Translational and Advanced Bioimaging Laboratory, Hong Kong University of Science and Technology, Hong Kong 999077, China
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