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Liu W, Zhang Q, Li Q, Wang S. Contrastive and uncertainty-aware nuclei segmentation and classification. Comput Biol Med 2024; 178:108667. [PMID: 38850962 DOI: 10.1016/j.compbiomed.2024.108667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 04/18/2024] [Accepted: 05/26/2024] [Indexed: 06/10/2024]
Abstract
Nuclei segmentation and classification play a crucial role in pathology diagnosis, enabling pathologists to analyze cellular characteristics accurately. Overlapping cluster nuclei, misdetection of small-scale nuclei, and pleomorphic nuclei-induced misclassification have always been major challenges in the nuclei segmentation and classification tasks. To this end, we introduce an auxiliary task of nuclei boundary-guided contrastive learning to enhance the representativeness and discriminative power of visual features, particularly for addressing the challenge posed by the unclear contours of adherent nuclei and small nuclei. In addition, misclassifications resulting from pleomorphic nuclei often exhibit low classification confidence, indicating a high level of uncertainty. To mitigate misclassification, we capitalize on the characteristic clustering of similar cells to propose a locality-aware class embedding module, offering a regional perspective to capture category information. Moreover, we address uncertain classification in densely aggregated nuclei by designing a top-k uncertainty attention module that leverages deep features to enhance shallow features, thereby improving the learning of contextual semantic information. We demonstrate that the proposed network outperforms the off-the-shelf methods in both nuclei segmentation and classification experiments, achieving the state-of-the-art performance.
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Affiliation(s)
- Wenxi Liu
- College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China.
| | - Qing Zhang
- College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China.
| | - Qi Li
- College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China.
| | - Shu Wang
- College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China.
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2
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Duque-Vazquez EF, Sanchez-Yanez RE, Saldaña-Robles N, León-Galván MF, Cepeda-Negrete J. HeLa cell segmentation using digital image processing. Heliyon 2024; 10:e26520. [PMID: 38434298 PMCID: PMC10907640 DOI: 10.1016/j.heliyon.2024.e26520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 11/28/2023] [Accepted: 02/14/2024] [Indexed: 03/05/2024] Open
Abstract
Computational cell segmentation is a vital area of research, particularly in the analysis of images of cancer cells. The use of cell lines, such as the widely utilized HeLa cell line, is crucial for studying cancer. While deep learning algorithms have been commonly employed for cell segmentation, their resource and data requirements can be impractical for many laboratories. In contrast, image processing algorithms provide a promising alternative due to their effectiveness and minimal resource demands. This article presents the development of an algorithm utilizing digital image processing to segment the nucleus and shape of HeLa cells. The research aims to segment the cell shape in the image center and accurately identify the nucleus. The study uses and processes 300 images obtained from Serial Block-Face Scanning Electron Microscopy (SBF-SEM). For cell segmentation, the morphological operation of erosion was used to separate the cells, and through distance calculation, the cell located at the center of the image was selected. Subsequently, the eroded shape was employed to restore the original cell shape. The nucleus segmentation uses parameters such as distances and sizes, along with the implementation of verification stages to ensure accurate detection. The accuracy of the algorithm is demonstrated by comparing it with another algorithm meeting the same conditions, using four segmentation similarity metrics. The evaluation results rank the proposed algorithm as the superior choice, highlighting significant outcomes. The algorithm developed represents a crucial initial step towards more accurate disease analysis. In addition, it enables the measurement of shapes and the identification of morphological alterations, damages, and changes in organelles within the cell, which can be vital for diagnostic purposes.
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Affiliation(s)
- Edgar F Duque-Vazquez
- Universidad de Guanajuato DICIVA, Ex Hacienda El Copal km 9; carretera Irapuato-Silao; A.P. 311, Irapuato, 36500 Guanajuato, Mexico
| | - Raul E Sanchez-Yanez
- Universidad de Guanajuato DICIS, Carretera Salamanca - Valle de Santiago km 3.5 + 1.8 Comunidad de Palo Blanco, Salamanca, 36885 Guanajuato, Mexico
| | - Noe Saldaña-Robles
- Universidad de Guanajuato DICIVA, Ex Hacienda El Copal km 9; carretera Irapuato-Silao; A.P. 311, Irapuato, 36500 Guanajuato, Mexico
| | - Ma Fabiola León-Galván
- Universidad de Guanajuato DICIVA, Ex Hacienda El Copal km 9; carretera Irapuato-Silao; A.P. 311, Irapuato, 36500 Guanajuato, Mexico
| | - Jonathan Cepeda-Negrete
- Universidad de Guanajuato DICIVA, Ex Hacienda El Copal km 9; carretera Irapuato-Silao; A.P. 311, Irapuato, 36500 Guanajuato, Mexico
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3
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Chen J, Yang G, Liu A, Chen X, Liu J. SFE-Net: Spatial-Frequency Enhancement Network for robust nuclei segmentation in histopathology images. Comput Biol Med 2024; 171:108131. [PMID: 38447498 DOI: 10.1016/j.compbiomed.2024.108131] [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: 11/03/2023] [Revised: 01/19/2024] [Accepted: 02/04/2024] [Indexed: 03/08/2024]
Abstract
Morphological features of individual nuclei serve as a dependable foundation for pathologists in making accurate diagnoses. Existing methods that rely on spatial information for feature extraction have achieved commendable results in nuclei segmentation tasks. However, these approaches are not sufficient to extract edge information of nuclei with small sizes and blurred outlines. Moreover, the lack of attention to the interior of the nuclei leads to significant internal inconsistencies. To address these challenges, we introduce a novel Spatial-Frequency Enhancement Network (SFE-Net) to incorporate spatial-frequency features and promote intra-nuclei consistency for robust nuclei segmentation. Specifically, SFE-Net incorporates a distinctive Spatial-Frequency Feature Extraction module and a Spatial-Guided Feature Enhancement module, which are designed to preserve spatial-frequency information and enhance feature representation respectively, to achieve comprehensive extraction of edge information. Furthermore, we introduce the Label-Guided Distillation method, which utilizes semantic features to guide the segmentation network in strengthening boundary constraints and learning the intra-nuclei consistency of individual nuclei, to improve the robustness of nuclei segmentation. Extensive experiments on three publicly available histopathology image datasets (MoNuSeg, TNBC and CryoNuSeg) demonstrate the superiority of our proposed method, which achieves 79.23%, 81.96% and 73.26% Aggregated Jaccard Index, respectively. The proposed model is available at https://github.com/jinshachen/SFE-Net.
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Affiliation(s)
- Jinsha Chen
- School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China
| | - Gang Yang
- School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China
| | - Aiping Liu
- School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China
| | - Xun Chen
- School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China
| | - Ji Liu
- School of Biomedical Engineering, Division of Life Sciences and Medicine, School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China.
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4
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Gurkar AU, Gerencser AA, Mora AL, Nelson AC, Zhang AR, Lagnado AB, Enninful A, Benz C, Furman D, Beaulieu D, Jurk D, Thompson EL, Wu F, Rodriguez F, Barthel G, Chen H, Phatnani H, Heckenbach I, Chuang JH, Horrell J, Petrescu J, Alder JK, Lee JH, Niedernhofer LJ, Kumar M, Königshoff M, Bueno M, Sokka M, Scheibye-Knudsen M, Neretti N, Eickelberg O, Adams PD, Hu Q, Zhu Q, Porritt RA, Dong R, Peters S, Victorelli S, Pengo T, Khaliullin T, Suryadevara V, Fu X, Bar-Joseph Z, Ji Z, Passos JF. Spatial mapping of cellular senescence: emerging challenges and opportunities. NATURE AGING 2023; 3:776-790. [PMID: 37400722 PMCID: PMC10505496 DOI: 10.1038/s43587-023-00446-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 05/30/2023] [Indexed: 07/05/2023]
Abstract
Cellular senescence is a well-established driver of aging and age-related diseases. There are many challenges to mapping senescent cells in tissues such as the absence of specific markers and their relatively low abundance and vast heterogeneity. Single-cell technologies have allowed unprecedented characterization of senescence; however, many methodologies fail to provide spatial insights. The spatial component is essential, as senescent cells communicate with neighboring cells, impacting their function and the composition of extracellular space. The Cellular Senescence Network (SenNet), a National Institutes of Health (NIH) Common Fund initiative, aims to map senescent cells across the lifespan of humans and mice. Here, we provide a comprehensive review of the existing and emerging methodologies for spatial imaging and their application toward mapping senescent cells. Moreover, we discuss the limitations and challenges inherent to each technology. We argue that the development of spatially resolved methods is essential toward the goal of attaining an atlas of senescent cells.
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Affiliation(s)
- Aditi U Gurkar
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Ana L Mora
- Dorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, the Ohio State University, Columbus, OH, USA
| | - Andrew C Nelson
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Anru R Zhang
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine and Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Anthony B Lagnado
- Department of Physiology and Biomedical Engineering, Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA
| | - Archibald Enninful
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | | | - David Furman
- Buck Institute for Research on Aging, Novato, CA, USA
- Stanford 1000 Immunomes Project, Stanford School of Medicine, Stanford University, Stanford, CA, USA
- Instituto de Investigaciones en Medicina Traslacional (IIMT), Universidad Austral, Pilar, Argentina
| | - Delphine Beaulieu
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Diana Jurk
- Department of Physiology and Biomedical Engineering, Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA
| | - Elizabeth L Thompson
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Fei Wu
- Buck Institute for Research on Aging, Novato, CA, USA
| | - Fernanda Rodriguez
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Grant Barthel
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Hao Chen
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Hemali Phatnani
- Columbia University Irving Medical Center and New York Genome Center, Columbia University, New York, NY, USA
| | | | - Jeffrey H Chuang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Jeremy Horrell
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI, USA
| | - Joana Petrescu
- Columbia University Irving Medical Center and New York Genome Center, Columbia University, New York, NY, USA
| | - Jonathan K Alder
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jun Hee Lee
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Laura J Niedernhofer
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Manoj Kumar
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, Stanford, CA, USA
| | - Melanie Königshoff
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Marta Bueno
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Miiko Sokka
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI, USA
| | | | - Nicola Neretti
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI, USA
| | - Oliver Eickelberg
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Peter D Adams
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Qianjiang Hu
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Quan Zhu
- University of California, San Diego, CA, USA
| | - Rebecca A Porritt
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Runze Dong
- Department of Biochemistry, Institute for Protein Design and Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA, USA
| | - Samuel Peters
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Stella Victorelli
- Department of Physiology and Biomedical Engineering, Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA
| | - Thomas Pengo
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Timur Khaliullin
- Dorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, the Ohio State University, Columbus, OH, USA
| | - Vidyani Suryadevara
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, Stanford, CA, USA
| | - Xiaonan Fu
- Department of Biochemistry, Institute for Protein Design and Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA, USA
| | - Ziv Bar-Joseph
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Zhicheng Ji
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine and Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - João F Passos
- Department of Physiology and Biomedical Engineering, Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA.
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5
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Cuny AP, Ponti A, Kündig T, Rudolf F, Stelling J. Cell region fingerprints enable highly precise single-cell tracking and lineage reconstruction. Nat Methods 2022; 19:1276-1285. [PMID: 36138173 DOI: 10.1038/s41592-022-01603-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 08/02/2022] [Indexed: 11/09/2022]
Abstract
Experimental studies of cell growth, inheritance and their associated processes by microscopy require accurate single-cell observations of sufficient duration to reconstruct the genealogy. However, cell tracking-assigning identical cells on consecutive images to a track-is often challenging, resulting in laborious manual verification. Here, we propose fingerprints to identify problematic assignments rapidly. A fingerprint distance compares the structural information contained in the low frequencies of a Fourier transform to measure the similarity between cells in two consecutive images. We show that fingerprints are broadly applicable across cell types and image modalities, provided the image has sufficient structural information. Our tracker (TracX) uses fingerprints to reject unlikely assignments, thereby increasing tracking performance on published and newly generated long-term data sets. For Saccharomyces cerevisiae, we propose a comprehensive model for cell size control at the single-cell and population level centered on the Whi5 regulator, demonstrating how precise tracking can help uncover previously undescribed single-cell biology.
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Affiliation(s)
- Andreas P Cuny
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Aaron Ponti
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Tomas Kündig
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Fabian Rudolf
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Jörg Stelling
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland. .,Swiss Institute of Bioinformatics, Basel, Switzerland.
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6
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Qin J, He Y, Zhou Y, Zhao J, Ding B. REU-Net: Region-enhanced nuclei segmentation network. Comput Biol Med 2022; 146:105546. [DOI: 10.1016/j.compbiomed.2022.105546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 03/24/2022] [Accepted: 04/17/2022] [Indexed: 11/03/2022]
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7
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Cuny AP, Schlottmann FP, Ewald JC, Pelet S, Schmoller KM. Live cell microscopy: From image to insight. BIOPHYSICS REVIEWS 2022; 3:021302. [PMID: 38505412 PMCID: PMC10903399 DOI: 10.1063/5.0082799] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 03/18/2022] [Indexed: 03/21/2024]
Abstract
Live-cell microscopy is a powerful tool that can reveal cellular behavior as well as the underlying molecular processes. A key advantage of microscopy is that by visualizing biological processes, it can provide direct insights. Nevertheless, live-cell imaging can be technically challenging and prone to artifacts. For a successful experiment, many careful decisions are required at all steps from hardware selection to downstream image analysis. Facing these questions can be particularly intimidating due to the requirement for expertise in multiple disciplines, ranging from optics, biophysics, and programming to cell biology. In this review, we aim to summarize the key points that need to be considered when setting up and analyzing a live-cell imaging experiment. While we put a particular focus on yeast, many of the concepts discussed are applicable also to other organisms. In addition, we discuss reporting and data sharing strategies that we think are critical to improve reproducibility in the field.
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Affiliation(s)
| | - Fabian P. Schlottmann
- Interfaculty Institute of Cell Biology, University of Tuebingen, 72076 Tuebingen, Germany
| | - Jennifer C. Ewald
- Interfaculty Institute of Cell Biology, University of Tuebingen, 72076 Tuebingen, Germany
| | - Serge Pelet
- Department of Fundamental Microbiology, University of Lausanne, 1015 Lausanne, Switzerland
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8
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In vivo study of dose-dependent antioxidant efficacy of functionalized core-shell yttrium oxide nanoparticles. Naunyn Schmiedebergs Arch Pharmacol 2022; 395:593-606. [PMID: 35201389 PMCID: PMC8989852 DOI: 10.1007/s00210-022-02219-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 02/09/2022] [Indexed: 11/30/2022]
Abstract
Abstract Herein, we assess the dose-dependent antioxidant efficacy of ultrafine spherical functionalized core–shell yttrium oxide nanoparticles (YNPs) with a mean size of 7–8 nm and modified with poly EGMP (ethylene glycol methacrylate phosphate) and N-Fluorescein Acrylamide. The antioxidant properties of these nanoparticles were investigated in three groups of Sprague–Dawley rats (10 per group) exposed to environmental stress daily for 1 week and one control group. Groups 2 and 3 were intravenously injected twice a week with YNPs at 0.3 and 0.5 mg at 2nd and 5th day of environmental stress exposure respectively. Different samples of blood and serum were collected from all experimental groups at end of the experiment to measure oxidative biomarkers such as total antioxidant capacity (TAC), hydroxyl radical antioxidant capacity (HORAC), oxygen radical antioxidant capacity (ORAC), malondialdehyde (MDA), and oxidants concentration as hydrogen peroxide (H2O2). The liver, brain, and spleen tissues were collected for fluorescence imaging and histopathological examination in addition to brain tissue examination by transmission electron microscope (TEM). Inductively coupled plasma-mass spectrometry (ICP-MS) was used to estimate YNPs translocation and concentration in tissues which is consecutively dependent on the dose of administration. Depending on all results, poly EGMP YNPs (poly EGMP yttrium oxide nanoparticles) can act as a potent direct antioxidant in a dose-dependent manner with good permeability through blood–brain barrier (BBB). Also, the neuroprotective effect of YNPs opening the door to a new therapeutic approach for modulating oxidative stress–related neural disorders. Highlights • The dose-dependent antioxidant efficacy of ultrafine spherical functionalized core–shell yttrium oxide nanoparticles (YNPs) with a mean size of 7–8 nm and modified with poly EGMP (ethylene glycol methacrylate phosphate) and N-Fluorescein Acrylamide was assessed. • The dose of administration directly affecting the brain, liver, and spleen tissues distribution, retention, and uptake of YNPs and direct correlation between the absorbed amount and higher dose administered. • YNPs can act as a potent direct antioxidant in a dose-dependent manner with good permeability through blood–brain barrier (BBB). Graphical abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1007/s00210-022-02219-1.
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9
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Membrane marker selection for segmenting single cell spatial proteomics data. Nat Commun 2022; 13:1999. [PMID: 35422106 PMCID: PMC9010440 DOI: 10.1038/s41467-022-29667-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 03/25/2022] [Indexed: 12/21/2022] Open
Abstract
The ability to profile spatial proteomics at the single cell level enables the study of cell types, their spatial distribution, and interactions in several tissues and conditions. Current methods for cell segmentation in such studies rely on known membrane or cell boundary markers. However, for many tissues, an optimal set of markers is not known, and even within a tissue, different cell types may express different markers. Here we present RAMCES, a method that uses a convolutional neural network to learn the optimal markers for a new sample and outputs a weighted combination of the selected markers for segmentation. Testing RAMCES on several existing datasets indicates that it correctly identifies cell boundary markers, improving on methods that rely on a single marker or those that extend nuclei segmentations. Application to new spatial proteomics data demonstrates its usefulness for accurately assigning cell types based on the proteins expressed in segmented cells. Cell segmentation of single-cell spatial proteomics data remains a challenge and often relies on the selection of a membrane marker, which is not always known. Here, the authors introduce RAMCES, a method that selects the optimal membrane markers to use for more accurate cell segmentation.
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10
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Hajdowska K, Student S, Borys D. Graph based method for cell segmentation and detection in live-cell fluorescence microscope imaging. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103071] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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11
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Kruitbosch HT, Mzayek Y, Omlor S, Guerra P, Milias-Argeitis A. A convolutional neural network for segmentation of yeast cells without manual training annotations. Bioinformatics 2021; 38:1427-1433. [PMID: 34893817 PMCID: PMC8825468 DOI: 10.1093/bioinformatics/btab835] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 10/09/2021] [Accepted: 12/07/2021] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION Single-cell time-lapse microscopy is a ubiquitous tool for studying the dynamics of complex cellular processes. While imaging can be automated to generate very large volumes of data, the processing of the resulting movies to extract high-quality single-cell information remains a challenging task. The development of software tools that automatically identify and track cells is essential for realizing the full potential of time-lapse microscopy data. Convolutional neural networks (CNNs) are ideally suited for such applications, but require great amounts of manually annotated data for training, a time-consuming and tedious process. RESULTS We developed a new approach to CNN training for yeast cell segmentation based on synthetic data and present (i) a software tool for the generation of synthetic images mimicking brightfield images of budding yeast cells and (ii) a convolutional neural network (Mask R-CNN) for yeast segmentation that was trained on a fully synthetic dataset. The Mask R-CNN performed excellently on segmenting actual microscopy images of budding yeast cells, and a density-based spatial clustering algorithm (DBSCAN) was able to track the detected cells across the frames of microscopy movies. Our synthetic data creation tool completely bypassed the laborious generation of manually annotated training datasets, and can be easily adjusted to produce images with many different features. The incorporation of synthetic data creation into the development pipeline of CNN-based tools for budding yeast microscopy is a critical step toward the generation of more powerful, widely applicable and user-friendly image processing tools for this microorganism. AVAILABILITY AND IMPLEMENTATION The synthetic data generation code can be found at https://github.com/prhbrt/synthetic-yeast-cells. The Mask R-CNN as well as the tuning and benchmarking scripts can be found at https://github.com/ymzayek/yeastcells-detection-maskrcnn. We also provide Google Colab scripts that reproduce all the results of this work. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Herbert T Kruitbosch
- Center for Information Technology, University of Groningen, 9747 AJ Groningen, The Netherlands
| | - Yasmin Mzayek
- Center for Information Technology, University of Groningen, 9747 AJ Groningen, The Netherlands
| | - Sara Omlor
- Center for Information Technology, University of Groningen, 9747 AJ Groningen, The Netherlands
| | - Paolo Guerra
- Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, 9747 AG Groningen, The Netherlands
| | - Andreas Milias-Argeitis
- Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, 9747 AG Groningen, The Netherlands,To whom correspondence should be addressed.
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12
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Papagiannidis D, Bircham PW, Lüchtenborg C, Pajonk O, Ruffini G, Brügger B, Schuck S. Ice2 promotes ER membrane biogenesis in yeast by inhibiting the conserved lipin phosphatase complex. EMBO J 2021; 40:e107958. [PMID: 34617598 PMCID: PMC8591542 DOI: 10.15252/embj.2021107958] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 09/01/2021] [Accepted: 09/03/2021] [Indexed: 11/30/2022] Open
Abstract
Cells dynamically adapt organelle size to current physiological demand. Organelle growth requires membrane biogenesis and therefore needs to be coordinated with lipid metabolism. The endoplasmic reticulum (ER) can undergo massive expansion, but the underlying regulatory mechanisms are largely unclear. Here, we describe a genetic screen for factors involved in ER membrane expansion in budding yeast and identify the ER transmembrane protein Ice2 as a strong hit. We show that Ice2 promotes ER membrane biogenesis by opposing the phosphatidic acid phosphatase Pah1, called lipin in metazoa. Specifically, Ice2 inhibits the conserved Nem1‐Spo7 complex and thus suppresses the dephosphorylation and activation of Pah1. Furthermore, Ice2 cooperates with the transcriptional regulation of lipid synthesis genes and helps to maintain cell homeostasis during ER stress. These findings establish the control of the lipin phosphatase complex as an important mechanism for regulating ER membrane biogenesis.
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Affiliation(s)
- Dimitrios Papagiannidis
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance and Cell Networks Cluster of Excellence, Heidelberg, Germany.,Heidelberg University Biochemistry Center (BZH), Heidelberg, Germany
| | - Peter W Bircham
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance and Cell Networks Cluster of Excellence, Heidelberg, Germany
| | | | - Oliver Pajonk
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance and Cell Networks Cluster of Excellence, Heidelberg, Germany.,Heidelberg University Biochemistry Center (BZH), Heidelberg, Germany
| | - Giulia Ruffini
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance and Cell Networks Cluster of Excellence, Heidelberg, Germany.,Heidelberg University Biochemistry Center (BZH), Heidelberg, Germany
| | - Britta Brügger
- Heidelberg University Biochemistry Center (BZH), Heidelberg, Germany
| | - Sebastian Schuck
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance and Cell Networks Cluster of Excellence, Heidelberg, Germany.,Heidelberg University Biochemistry Center (BZH), Heidelberg, Germany
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13
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Yeast cell segmentation in microstructured environments with deep learning. Biosystems 2021; 211:104557. [PMID: 34634444 DOI: 10.1016/j.biosystems.2021.104557] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 09/09/2021] [Accepted: 09/30/2021] [Indexed: 11/23/2022]
Abstract
Cell segmentation is a major bottleneck in extracting quantitative single-cell information from microscopy data. The challenge is exasperated in the setting of microstructured environments. While deep learning approaches have proven useful for general cell segmentation tasks, previously available segmentation tools for the yeast-microstructure setting rely on traditional machine learning approaches. Here we present convolutional neural networks trained for multiclass segmenting of individual yeast cells and discerning these from cell-similar microstructures. An U-Net based semantic segmentaiton approach, as well as a direct instance segmentation approach with a Mask R-CNN are demonstrated. We give an overview of the datasets recorded for training, validating and testing the networks, as well as a typical use-case. We showcase the methods' contribution to segmenting yeast in microstructured environments with a typical systems or synthetic biology application. The models achieve robust segmentation results, outperforming the previous state-of-the-art in both accuracy and speed. The combination of fast and accurate segmentation is not only beneficial for a posteriori data processing, it also makes online monitoring of thousands of trapped cells or closed-loop optimal experimental design feasible from an image processing perspective. Code is and data samples are available at https://git.rwth-aachen.de/bcs/projects/tp/multiclass-yeast-seg.
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14
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Cheng HJ, Hsu CH, Hung CL, Lin CY. A review for Cell and Particle Tracking on Microscopy Images using Algorithms and Deep Learning Technologies. Biomed J 2021; 45:465-471. [PMID: 34628059 PMCID: PMC9421944 DOI: 10.1016/j.bj.2021.10.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 09/30/2021] [Accepted: 10/01/2021] [Indexed: 01/06/2023] Open
Abstract
Time-lapse microscopy images generated by biological experiments have been widely used for observing target activities, such as the motion trajectories and survival states. Based on these observations, biologists can conclude experimental results or present new hypotheses for several biological applications, i.e. virus research or drug design. Many methods or tools have been proposed in the past to observe cell and particle activities, which are defined as single cell tracking and single particle tracking problems, by using algorithms and deep learning technologies. In this article, a review for these works is presented in order to summarize the past methods and research topics at first, then points out the problems raised by these works, and finally proposes future research directions. The contributions of this article will help researchers to understand past development trends and further propose innovative technologies.
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Affiliation(s)
- Hui-Jun Cheng
- Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou 510095, China; Department of Computer Science and Information Engineering, Providence University, Taichung 43301, Taiwan
| | - Ching-Hsien Hsu
- Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan; Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology, School of Mathematics and Big Data, Foshan University, Foshan 528000, China; Department of Medical Research, China Medical University Hospital, China Medical University, Taiwan
| | - Che-Lun Hung
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; Department of Computer Science and Communication Engineering, Providence University, Taichung 43301, Taiwan
| | - Chun-Yuan Lin
- Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan; Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan 33302, Taiwan.
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15
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Abstract
Spatial transcriptomics is a rapidly growing field that promises to comprehensively characterize tissue organization and architecture at the single-cell or subcellular resolution. Such information provides a solid foundation for mechanistic understanding of many biological processes in both health and disease that cannot be obtained by using traditional technologies. The development of computational methods plays important roles in extracting biological signals from raw data. Various approaches have been developed to overcome technology-specific limitations such as spatial resolution, gene coverage, sensitivity, and technical biases. Downstream analysis tools formulate spatial organization and cell-cell communications as quantifiable properties, and provide algorithms to derive such properties. Integrative pipelines further assemble multiple tools in one package, allowing biologists to conveniently analyze data from beginning to end. In this review, we summarize the state of the art of spatial transcriptomic data analysis methods and pipelines, and discuss how they operate on different technological platforms.
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Affiliation(s)
- Ruben Dries
- Department of Medicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA
- Bioinformatics Graduate Program, Boston University, Boston, Massachusetts 02215, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA
| | - Jiaji Chen
- Department of Medicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA
| | - Natalie Del Rossi
- Department of Genetics and Genomic Sciences, Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Mohammed Muzamil Khan
- Department of Medicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA
- Bioinformatics Graduate Program, Boston University, Boston, Massachusetts 02215, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA
| | - Adriana Sistig
- Department of Genetics and Genomic Sciences, Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
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16
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Kumar S, Rullan M, Khammash M. Rapid prototyping and design of cybergenetic single-cell controllers. Nat Commun 2021; 12:5651. [PMID: 34561433 PMCID: PMC8463601 DOI: 10.1038/s41467-021-25754-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 08/26/2021] [Indexed: 12/22/2022] Open
Abstract
The design and implementation of synthetic circuits that operate robustly in the cellular context is fundamental for the advancement of synthetic biology. However, their practical implementation presents challenges due to low predictability of synthetic circuit design and time-intensive troubleshooting. Here, we present the Cyberloop, a testing framework to accelerate the design process and implementation of biomolecular controllers. Cellular fluorescence measurements are sent in real-time to a computer simulating candidate stochastic controllers, which in turn compute the control inputs and feed them back to the controlled cells via light stimulation. Applying this framework to yeast cells engineered with optogenetic tools, we examine and characterize different biomolecular controllers, test the impact of non-ideal circuit behaviors such as dilution on their operation, and qualitatively demonstrate improvements in controller function with certain network modifications. From this analysis, we derive conditions for desirable biomolecular controller performance, thereby avoiding pitfalls during its biological implementation.
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Affiliation(s)
- Sant Kumar
- Department of Biosystems Science and Engineering (D-BSSE), ETH Zürich, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Marc Rullan
- Department of Biosystems Science and Engineering (D-BSSE), ETH Zürich, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Mustafa Khammash
- Department of Biosystems Science and Engineering (D-BSSE), ETH Zürich, Mattenstrasse 26, 4058, Basel, Switzerland.
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17
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Zhu Y, Meijering E. Automatic Improvement of Deep Learning Based Cell Segmentation in Time-Lapse Microscopy by Neural Architecture Search. Bioinformatics 2021; 37:4844-4850. [PMID: 34329376 PMCID: PMC8665766 DOI: 10.1093/bioinformatics/btab556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 07/18/2021] [Accepted: 08/04/2021] [Indexed: 11/25/2022] Open
Abstract
Motivation Live cell segmentation is a crucial step in biological image analysis and is also a challenging task because time-lapse microscopy cell sequences usually exhibit complex spatial structures and complicated temporal behaviors. In recent years, numerous deep learning-based methods have been proposed to tackle this task and obtained promising results. However, designing a network with excellent performance requires professional knowledge and expertise and is very time-consuming and labor-intensive. Recently emerged neural architecture search (NAS) methods hold great promise in eliminating these disadvantages, because they can automatically search an optimal network for the task. Results We propose a novel NAS-based solution for deep learning-based cell segmentation in time-lapse microscopy images. Different from current NAS methods, we propose (i) jointly searching non-repeatable micro architectures to construct the macro network for exploring greater NAS potential and better performance and (ii) defining a specific search space suitable for the live cell segmentation task, including the incorporation of a convolutional long short-term memory network for exploring the temporal information in time-lapse sequences. Comprehensive evaluations on the 2D datasets from the cell tracking challenge demonstrate the competitiveness of the proposed method compared to the state of the art. The experimental results show that the method is capable of achieving more consistent top performance across all ten datasets than the other challenge methods. Availabilityand implementation The executable files of the proposed method as well as configurations for each dataset used in the presented experiments will be available for non-commercial purposes from https://github.com/291498346/nas_cellseg. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yanming Zhu
- School of Computer Science and Engineering, University of New South Wales, Sydney, 2052, Australia
| | - Erik Meijering
- School of Computer Science and Engineering, University of New South Wales, Sydney, 2052, Australia
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18
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Prinke P, Haueisen J, Klee S, Rizqie MQ, Supriyanto E, König K, Breunig HG, Piątek Ł. Automatic segmentation of skin cells in multiphoton data using multi-stage merging. Sci Rep 2021; 11:14534. [PMID: 34267247 PMCID: PMC8282875 DOI: 10.1038/s41598-021-93682-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 06/27/2021] [Indexed: 01/10/2023] Open
Abstract
We propose a novel automatic segmentation algorithm that separates the components of human skin cells from the rest of the tissue in fluorescence data of three-dimensional scans using non-invasive multiphoton tomography. The algorithm encompasses a multi-stage merging on preprocessed superpixel images to ensure independence from a single empirical global threshold. This leads to a high robustness of the segmentation considering the depth-dependent data characteristics, which include variable contrasts and cell sizes. The subsequent classification of cell cytoplasm and nuclei are based on a cell model described by a set of four features. Two novel features, a relationship between outer cell and inner nucleus (OCIN) and a stability index, were derived. The OCIN feature describes the topology of the model, while the stability index indicates segment quality in the multi-stage merging process. These two new features, combined with the local gradient magnitude and compactness, are used for the model-based fuzzy evaluation of the cell segments. We exemplify our approach on an image stack with 200 × 200 × 100 μm3, including the skin layers of the stratum spinosum and the stratum basale of a healthy volunteer. Our image processing pipeline contributes to the fully automated classification of human skin cells in multiphoton data and provides a basis for the detection of skin cancer using non-invasive optical biopsy.
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Affiliation(s)
- Philipp Prinke
- Institute for Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693, Ilmenau, Germany.
| | - Jens Haueisen
- Institute for Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693, Ilmenau, Germany
| | - Sascha Klee
- Institute for Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693, Ilmenau, Germany.,Division Biostatistics and Data Science, Department of General Health Studies, Karl Landsteiner University of Health Sciences, Dr. Karl-Dorrek-Straße 30, 3500, Krems, Austria
| | - Muhammad Qurhanul Rizqie
- Institute for Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693, Ilmenau, Germany.,Informatics Engineering Program, Universitas Sriwijaya, Palembang, South Sumatera, Indonesia
| | - Eko Supriyanto
- Institute for Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693, Ilmenau, Germany.,IJN-UTM Cardiovascular Engineering Centre, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia
| | - Karsten König
- Department of Biophotonics and Laser Technology, Saarland University, Campus A5.1, 66123, Saarbrücken, Germany.,JenLab GmbH, Johann-Hittorf-Straße 8, 12489, Berlin, Germany
| | - Hans Georg Breunig
- Department of Biophotonics and Laser Technology, Saarland University, Campus A5.1, 66123, Saarbrücken, Germany.,JenLab GmbH, Johann-Hittorf-Straße 8, 12489, Berlin, Germany
| | - Łukasz Piątek
- Institute for Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693, Ilmenau, Germany.,Department of Artificial Intelligence, University of Information Technology and Management, H. Sucharskiego 2 Str, 35-225, Rzeszów, Poland
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19
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Wang Z, Zhang L, Zhao M, Wang Y, Bai H, Wang Y, Rui C, Fan C, Li J, Li N, Liu X, Wang Z, Si Y, Feng A, Li M, Zhang Q, Yang Z, Wang M, Wu W, Cao Y, Qi L, Zeng X, Geng L, An R, Li P, Liu Z, Qiao Q, Zhu W, Mo W, Liao Q, Xu W. Deep Neural Networks Offer Morphologic Classification and Diagnosis of Bacterial Vaginosis. J Clin Microbiol 2021; 59:e02236-20. [PMID: 33148709 PMCID: PMC8111127 DOI: 10.1128/jcm.02236-20] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 11/01/2020] [Indexed: 11/20/2022] Open
Abstract
Bacterial vaginosis (BV) is caused by the excessive and imbalanced growth of bacteria in vagina, affecting 30 to 50% of women. Gram staining followed by Nugent scoring based on bacterial morphotypes under the microscope is considered the gold standard for BV diagnosis; this method is often labor-intensive and time-consuming, and results vary from person to person. We developed and optimized a convolutional neural network (CNN) model and evaluated its ability to automatically identify and classify three categories of Nugent scores from microscope images. The CNN model was first established with a panel of microscopic images with Nugent scores determined by experts. The model was trained by minimizing the cross-entropy loss function and optimized by using a momentum optimizer. The separate test sets of images collected from three hospitals were evaluated by the CNN model. The CNN model consisted of 25 convolutional layers, 2 pooling layers, and a fully connected layer. The model obtained 82.4% sensitivity and 96.6% specificity with the 5,815 validation images when altered vaginal flora and BV were considered the positive samples, which was better than the rates achieved by top-level technologists and obstetricians in China. The capability of our model for generalization was so strong that it exhibited 75.1% accuracy in three categories of Nugent scores on the independent test set of 1,082 images, which was 6.6% higher than the average of three technologists, who are hold bachelor's degrees in medicine and are qualified to make diagnostic decisions. When three technologists ran one specimen in triplicate, the precision of three categories of Nugent scores was 54.0%. One hundred three samples diagnosed by two technologists on different days showed a repeatability of 90.3%. The CNN model outperformed human health care practitioners in terms of accuracy and stability for three categories of Nugent score diagnosis. The deep learning model may offer translational applications in automating diagnosis of bacterial vaginosis with proper supporting hardware.
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Affiliation(s)
- Zhongxiao Wang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Lei Zhang
- Department of Obstetrics and Gynecology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Min Zhao
- Peking University First Hospital, Beijing, China
| | - Ying Wang
- Department of Obstetrics and Gynecology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Huihui Bai
- Beijing Obstetrics and Gynecology Hospital, Capital Medical University Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Yufeng Wang
- Department of Obstetrics and Gynecology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Can Rui
- Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China
| | - Chong Fan
- Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China
| | - Jiao Li
- The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Na Li
- The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xinhuan Liu
- Peking University Third Hospital, Beijing, China
| | - Zitao Wang
- The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China
| | - Yanyan Si
- Binzhou Medical University Hospital, Binzhou, China
| | - Andrea Feng
- Beijing HarMoniCare Women's and Children's Hospital, Beijing, China
| | - Mingxuan Li
- Suzhou Turing Microbial Technologies Co., Ltd., Suzhou, China
- Beijing Turing Microbial Technologies Co., Ltd., Beijing, China
| | - Qiongqiong Zhang
- Department of Obstetrics and Gynecology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
- School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Zhe Yang
- Department of Physics, Tsinghua University, Beijing, China
| | - Mengdi Wang
- Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey, USA
| | - Wei Wu
- Suzhou Turing Microbial Technologies Co., Ltd., Suzhou, China
- Beijing Turing Microbial Technologies Co., Ltd., Beijing, China
| | - Yang Cao
- Suzhou Turing Microbial Technologies Co., Ltd., Suzhou, China
- Beijing Turing Microbial Technologies Co., Ltd., Beijing, China
| | - Lin Qi
- The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Xin Zeng
- Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China
| | - Li Geng
- Peking University Third Hospital, Beijing, China
| | - Ruifang An
- The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Ping Li
- Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China
| | - Zhaohui Liu
- Beijing Obstetrics and Gynecology Hospital, Capital Medical University Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Qiao Qiao
- The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China
| | - Weipei Zhu
- The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Weike Mo
- Suzhou Turing Microbial Technologies Co., Ltd., Suzhou, China
- Beijing Turing Microbial Technologies Co., Ltd., Beijing, China
- Shanghai East Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Qinping Liao
- Department of Obstetrics and Gynecology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
- School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Wei Xu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
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20
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Abstract
Synthetic biology has so far made limited use of mathematical models, mostly because their inference has been traditionally perceived as expensive and/or difficult. We have recently demonstrated how in silico simulations and in vitro/vivo experiments can be integrated to develop a cyber-physical platform that automates model calibration and leads to saving 60-80% of the effort. In this book chapter, we illustrate the protocol used to attain such results. By providing a comprehensive list of steps and pointing the reader to the code we use to operate our platform, we aim at providing synthetic biologists with an additional tool to accelerate the pace at which the field progresses toward applications.
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21
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Lv S, Chu Y, Zhang P, Ma S, Zhao M, Wang Z, Gu Y, Sun X. Improved efficiency of urine cell image segmentation using droplet microfluidics technology. Cytometry A 2020; 99:722-731. [PMID: 33342063 DOI: 10.1002/cyto.a.24296] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 11/25/2020] [Accepted: 12/16/2020] [Indexed: 12/12/2022]
Abstract
Recent advances in the recognition of biological samples using machine vision have made this technology increasingly important in research and detection. Image segmentation is an important step in this process. This study focuses on how to reduce the interference factors such as the overlap between different types (or within the same type) of urine cells according to microfluidics and improve the machine vision segmentation accuracy for cell images. In this study, we demonstrate that the platform can realize this hypothesis using urine cell image segmentation as an example application. We first discuss the reported urine cell droplet microfluidic chip system, which can realize the test conditions in which urine cells are encapsulated in the droplet and isolated from salt crystallization and/or bacteria and other urine-formed elements. Then, based on the analysis conditions set in the aforementioned experiment, the proportions of red blood cells, white blood cells, and squamous epithelial cells covered by various formed elements in the total urine cells in the same urine sample are measured. We simultaneously analyze the percentage of urine cells covered by salt crystallization and the incidence of overlapping between urine cells. Finally, the Otsu algorithm is used to segment the urine cell images encapsulated by the droplet and the urine cell images not encapsulated by the droplet, and the Dice, Jaccard, precision, and recall values are calculated. The results suggest that the method of encapsulating single cells based on droplets can improve the image segmentation effect without optimizing the algorithm.
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Affiliation(s)
- Shuxing Lv
- School of Medical Laboratory, Tianjin Medical University, Tianjin, China
| | - Yuying Chu
- School of Medical Laboratory, Tianjin Medical University, Tianjin, China
| | - Panpan Zhang
- North China University of Science and Technology Affiliated Hospital, Tangshan, China
| | - Sike Ma
- Engineering Research Center of Learning-Based Intelligent System, Ministry of Education of China, Tianjin University of Technology, Tianjin, China
| | - Meng Zhao
- Engineering Research Center of Learning-Based Intelligent System, Ministry of Education of China, Tianjin University of Technology, Tianjin, China
| | - Zhexiang Wang
- School of Medical Laboratory, Tianjin Medical University, Tianjin, China
| | - Yajun Gu
- School of Medical Laboratory, Tianjin Medical University, Tianjin, China
| | - Xuguo Sun
- School of Medical Laboratory, Tianjin Medical University, Tianjin, China
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22
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Lee M, Lee YH, Song J, Kim G, Jo Y, Min H, Kim CH, Park Y. Deep-learning-based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells. eLife 2020; 9:49023. [PMID: 33331817 PMCID: PMC7817186 DOI: 10.7554/elife.49023] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 12/16/2020] [Indexed: 12/14/2022] Open
Abstract
The immunological synapse (IS) is a cell-cell junction between a T cell and a professional antigen-presenting cell. Since the IS formation is a critical step for the initiation of an antigen-specific immune response, various live-cell imaging techniques, most of which rely on fluorescence microscopy, have been used to study the dynamics of IS. However, the inherent limitations associated with the fluorescence-based imaging, such as photo-bleaching and photo-toxicity, prevent the long-term assessment of dynamic changes of IS with high frequency. Here, we propose and experimentally validate a label-free, volumetric, and automated assessment method for IS dynamics using a combinational approach of optical diffraction tomography and deep learning-based segmentation. The proposed method enables an automatic and quantitative spatiotemporal analysis of IS kinetics of morphological and biochemical parameters associated with IS dynamics, providing a new option for immunological research.
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Affiliation(s)
- Moosung Lee
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
| | - Young-Ho Lee
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.,Curocell Inc, Daejeon, Republic of Korea
| | - Jinyeop Song
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
| | - Geon Kim
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
| | - YoungJu Jo
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
| | | | - Chan Hyuk Kim
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - YongKeun Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
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23
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Establishment of a morphological atlas of the Caenorhabditis elegans embryo using deep-learning-based 4D segmentation. Nat Commun 2020; 11:6254. [PMID: 33288755 PMCID: PMC7721714 DOI: 10.1038/s41467-020-19863-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Accepted: 11/02/2020] [Indexed: 01/17/2023] Open
Abstract
The invariant development and transparent body of the nematode Caenorhabditis elegans enables complete delineation of cell lineages throughout development. Despite extensive studies of cell division, cell migration and cell fate differentiation, cell morphology during development has not yet been systematically characterized in any metazoan, including C. elegans. This knowledge gap substantially hampers many studies in both developmental and cell biology. Here we report an automatic pipeline, CShaper, which combines automated segmentation of fluorescently labeled membranes with automated cell lineage tracing. We apply this pipeline to quantify morphological parameters of densely packed cells in 17 developing C. elegans embryos. Consequently, we generate a time-lapse 3D atlas of cell morphology for the C. elegans embryo from the 4- to 350-cell stages, including cell shape, volume, surface area, migration, nucleus position and cell-cell contact with resolved cell identities. We anticipate that CShaper and the morphological atlas will stimulate and enhance further studies in the fields of developmental biology, cell biology and biomechanics. The systematic characterization of C. elegans morphology during development has yet to be performed. Here, the authors produce a 3D atlas of C. elegans morphology from 17 embryos and 54 developmental stages, using an automated pipeline, CShaper (combining segmentation of fluorescently labeled membranes with automated cell lineage tracing).
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Awasthi N, Katare P, Gorthi SS, Yalavarthy PK. Guided filter based image enhancement for focal error compensation in low cost automated histopathology microscopic system. JOURNAL OF BIOPHOTONICS 2020; 13:e202000123. [PMID: 33245636 DOI: 10.1002/jbio.202000123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 08/11/2020] [Accepted: 08/12/2020] [Indexed: 06/11/2023]
Abstract
Low-cost automated histopathology microscopy systems usually suffer from optical imperfections, producing images that are slightly Out of Focus (OoF). In this work, a guided filter (GF) based image preprocessing is proposed for compensating focal errors and its efficacy is demonstrated on images of healthy and malaria infected red blood cells (h-RBCs and i-RBCs), and PAP smears. Since contrast enhancement has been widely used as an image preprocessing step for the analysis of histopathology images, a systematic comparison is made with six such prominently used methods, namely Contrast Limited Adaptive Histogram Equalization (CLAHE), RIQMC-based optimal histogram matching (ROHIM), modified L0, Morphological Varying(MV)-Bitonic filter, unsharp mask filter and joint bilateral filter. The images enhanced using GF approach lead to better segmentation accuracy (upto 50% improvement over native images) and visual quality compared to other approaches, without any change in the color tones. Thus, the proposed GF approach is a viable solution for rectifying the OoF microscopy images without the loss of the valuable diagnostic information presented by the color tone.
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Affiliation(s)
- Navchetan Awasthi
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India
- Cardiovascular Research Centre, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard University, Cambridge, Massachusetts, USA
| | - Prateek Katare
- Department of Instrumentation and Applied Physics, Indian Institute of Science, Bangalore, India
| | - Sai Siva Gorthi
- Department of Instrumentation and Applied Physics, Indian Institute of Science, Bangalore, India
| | - Phaneendra K Yalavarthy
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India
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25
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Baybay EK, Esposito E, Hauf S. Pomegranate: 2D segmentation and 3D reconstruction for fission yeast and other radially symmetric cells. Sci Rep 2020; 10:16580. [PMID: 33024177 PMCID: PMC7538417 DOI: 10.1038/s41598-020-73597-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 09/14/2020] [Indexed: 11/09/2022] Open
Abstract
Three-dimensional (3D) segmentation of cells in microscopy images is crucial to accurately capture signals that extend across optical sections. Using brightfield images for segmentation has the advantage of being minimally phototoxic and leaving all other channels available for signals of interest. However, brightfield images only readily provide information for two-dimensional (2D) segmentation. In radially symmetric cells, such as fission yeast and many bacteria, this 2D segmentation can be computationally extruded into the third dimension. However, current methods typically make the simplifying assumption that cells are straight rods. Here, we report Pomegranate, a pipeline that performs the extrusion into 3D using spheres placed along the topological skeletons of the 2D-segmented regions. The diameter of these spheres adapts to the cell diameter at each position. Thus, Pomegranate accurately represents radially symmetric cells in 3D even if cell diameter varies and regardless of whether a cell is straight, bent or curved. We have tested Pomegranate on fission yeast and demonstrate its ability to 3D segment wild-type cells as well as classical size and shape mutants. The pipeline is available as a macro for the open-source image analysis software Fiji/ImageJ. 2D segmentations created within or outside Pomegranate can serve as input, thus making this a valuable extension to the image analysis portfolio already available for fission yeast and other radially symmetric cell types.
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Affiliation(s)
- Erod Keaton Baybay
- Department of Biological Sciences and Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA, USA.
| | - Eric Esposito
- Department of Biological Sciences and Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA, USA
| | - Silke Hauf
- Department of Biological Sciences and Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA, USA.
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Choi S, Lee H, Lee S, Park I, Kim YS, Key J, Lee SY, Yang S, Lee SW. A novel automatic segmentation and tracking method to measure cellular dielectrophoretic mobility from individual cell trajectories for high throughput assay. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 195:105662. [PMID: 32712504 DOI: 10.1016/j.cmpb.2020.105662] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 07/09/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE The dielectrophoresis (DEP) technique is increasingly being recognised as a potentially valuable tool for non-contact manipulation of numerous cells as well as for biological single cell analysis with non-invasive characterisation of a cell's electrical properties. Several studies have attempted to track multiple cells to characterise their cellular DEP mobility. However, they encountered difficulties in simultaneously tracking the movement of a large number of individual cells in a bright-field image sequence because of interference from the background electrode pattern. Consequently, this present study aims to develop an automatic system for imaging-based characterisation of cellular DEP mobility, which enables the simultaneous tracking of several hundred of cells inside a microfluidic device. METHODS The proposed method for segmentation and tracking of cells consists of two main stages: pre-processing and particle centre localisation. In the pre-processing stage, background subtraction and contrast enhancement were performed to distinguish the cell region from the background image. In the particle centre localisation stage, the unmarked cell was automatically detected via graph-cut algorithm-based K-means clustering. RESULTS Our algorithm enabled segmentation and tracking of numerous Michigan Cancer Foundation-7 (MCF-7) cell trajectories while the DEP force was oscillated between positive and negative. The cell tracking accuracy and cell count capability was at least 90% of the total number of cells with the newly developed algorithm. In addition, the cross-over frequency was measured by analysing the segmented and tracked trajectory data of the cellular movements caused by the positive and negative DEP force. The measured cross-over frequency was compared with previous results. The multi-cellular movements investigation based on the measured cross-over frequency was repeated until the viability of cells was unchanged in the same environment as in a microfluidic device. The results were statistically consistent, indicating that the developed algorithm was reliable for the investigation of DEP cellular mobility. CONCLUSION This study developed a powerful platform to simultaneously measure the DEP-induced trajectories of numerous cells, and to investigate in a robust, efficient, and accurate manner the DEP properties at both the single cell and cell ensemble level.
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Affiliation(s)
- Seungyeop Choi
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea
| | - Hyunwoo Lee
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea
| | - Sena Lee
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea
| | - Insu Park
- Holonyak Micro and Nanotechnology Laboratory, University of Illinois, Urbana, IL, USA
| | - Yoon Suk Kim
- Department of Biomedical Laboratory Science, Yonsei University, Wonju 26493, Republic of Korea
| | - Jaehong Key
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea
| | - Sei Young Lee
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea
| | - Sejung Yang
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea.
| | - Sang Woo Lee
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea.
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27
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Ren H, Zhao M, Liu B, Yao R, Liu Q, Ren Z, Wu Z, Gao Z, Yang X, Tang C. Cellbow: a robust customizable cell segmentation program. QUANTITATIVE BIOLOGY 2020. [DOI: 10.1007/s40484-020-0213-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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28
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Korfhage N, Mühling M, Ringshandl S, Becker A, Schmeck B, Freisleben B. Detection and segmentation of morphologically complex eukaryotic cells in fluorescence microscopy images via feature pyramid fusion. PLoS Comput Biol 2020; 16:e1008179. [PMID: 32898132 PMCID: PMC7523959 DOI: 10.1371/journal.pcbi.1008179] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 09/29/2020] [Accepted: 07/22/2020] [Indexed: 11/19/2022] Open
Abstract
Detection and segmentation of macrophage cells in fluorescence microscopy images is a challenging problem, mainly due to crowded cells, variation in shapes, and morphological complexity. We present a new deep learning approach for cell detection and segmentation that incorporates previously learned nucleus features. A novel fusion of feature pyramids for nucleus detection and segmentation with feature pyramids for cell detection and segmentation is used to improve performance on a microscopic image dataset created by us and provided for public use, containing both nucleus and cell signals. Our experimental results indicate that cell detection and segmentation performance significantly benefit from the fusion of previously learned nucleus features. The proposed feature pyramid fusion architecture clearly outperforms a state-of-the-art Mask R-CNN approach for cell detection and segmentation with relative mean average precision improvements of up to 23.88% and 23.17%, respectively.
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Affiliation(s)
- Nikolaus Korfhage
- Department of Mathematics and Computer Science, Philipps-Universität Marburg, Marburg, Germany
| | - Markus Mühling
- Department of Mathematics and Computer Science, Philipps-Universität Marburg, Marburg, Germany
| | - Stephan Ringshandl
- LOEWE-Center for Synthetic Microbiology, Philipps-Universität Marburg, Marburg, Germany
| | - Anke Becker
- LOEWE-Center for Synthetic Microbiology, Philipps-Universität Marburg, Marburg, Germany
| | - Bernd Schmeck
- Institute for Lung Research, Universities of Gießen and Marburg Lung Center, Marburg, Germany
- Department of Medicine, Pulmonary and Critical Care Medicine, Philipps-Universität Marburg, Germany
| | - Bernd Freisleben
- Department of Mathematics and Computer Science, Philipps-Universität Marburg, Marburg, Germany
- LOEWE-Center for Synthetic Microbiology, Philipps-Universität Marburg, Marburg, Germany
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29
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Lu AX, Zarin T, Hsu IS, Moses AM. YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells. Bioinformatics 2020; 35:4525-4527. [PMID: 31095270 PMCID: PMC6821424 DOI: 10.1093/bioinformatics/btz402] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 03/22/2019] [Accepted: 05/07/2019] [Indexed: 01/01/2023] Open
Abstract
SUMMARY We introduce YeastSpotter, a web application for the segmentation of yeast microscopy images into single cells. YeastSpotter is user-friendly and generalizable, reducing the computational expertise required for this critical preprocessing step in many image analysis pipelines. AVAILABILITY AND IMPLEMENTATION YeastSpotter is available at http://yeastspotter.csb.utoronto.ca/. Code is available at https://github.com/alexxijielu/yeast_segmentation. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Alex X Lu
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Taraneh Zarin
- Department of Cells and Systems Biology, University of Toronto, Toronto, ON, Canada
| | - Ian S Hsu
- Department of Cells and Systems Biology, University of Toronto, Toronto, ON, Canada
| | - Alan M Moses
- Department of Computer Science, University of Toronto, Toronto, ON, Canada.,Department of Cells and Systems Biology, University of Toronto, Toronto, ON, Canada.,Center for Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON, Canada
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30
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Sayour H, Kassem S, Canfarotta F, Czulak J, Mohamed M, Piletsky S. Biocompatibility and biodistribution of surface-modified yttrium oxide nanoparticles for potential theranostic applications. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2020; 27:19095-19107. [PMID: 30710327 DOI: 10.1007/s11356-019-04309-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 01/22/2019] [Indexed: 06/09/2023]
Abstract
The surface of ultrafine yttrium oxide nanoparticles (NPs) with mean size of 7-8 nm was modified with a functional polymer layer to improve their dispersion and impart fluorescent properties for imaging purposes. Surface functionalization was achieved by silanization of yttrium oxide NPs with 3-trimethoxysilylpropyl methacrylate followed by grafting of a co-polymer made of acrylic acid (AA) and ethylene glycol methacrylate phosphate (EGMP). The polymer shell decreases the surface energy of NPs, enhances their polarity, and, as a result, improves their colloidal stability. The synthesized NPs are capable of scavenging free radicals and for this reason have therapeutic potential that warrants further investigations. Furthermore, these stabilized core-shell NPs showed a very low cytotoxicity, confirming that the polymer shell sensibly improves the biocompatibility of bare yttrium oxide NPs, which are otherwise toxic on their own. Poly-EGMP yttrium NPs proved to be safe up to 0.1 mg/g body weight in 1 month old Sprague-Dawley rats, showing also the ability to cross the blood-brain barrier short time after tail injection. The surface modification of yttrium NPs here described allows these NPs to be potentially used in theranostics to reduce neurodegenerative damage due to the heat stress.
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Affiliation(s)
- Hossam Sayour
- Biomedical Chemistry Unit, Department of Chemistry and Nutritional Deficiency Disorders, Animal Health Research Institute, Giza, 12618, Egypt.
| | - Samr Kassem
- Department of Biotechnology, Animal Health Research Institute, Giza, 12618, Egypt
| | - Francesco Canfarotta
- MIP Diagnostics Ltd., University of Leicester, Fielding Johnson Building, University Road, Leicester, LE1 7RH, UK
| | - Joanna Czulak
- Department of Chemistry, University of Leicester, University Road, Leicester, LE1 7RH, UK
| | - Medhat Mohamed
- Department of Animal Medicine, Faculty of Veterinary Medicine, University of Kafr El-Sheikh, Kafr El-Sheikh, Egypt
| | - Sergey Piletsky
- Department of Chemistry, University of Leicester, University Road, Leicester, LE1 7RH, UK
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31
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Kang MS, Cha E, Kang E, Ye JC, Her NG, Oh JW, Nam DH, Kim MH, Yang S. Accuracy improvement of quantification information using super-resolution with convolutional neural network for microscopy images. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101846] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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32
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Perkins ML, Benzinger D, Arcak M, Khammash M. Cell-in-the-loop pattern formation with optogenetically emulated cell-to-cell signaling. Nat Commun 2020; 11:1355. [PMID: 32170129 PMCID: PMC7069979 DOI: 10.1038/s41467-020-15166-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 02/11/2020] [Indexed: 12/22/2022] Open
Abstract
Designing and implementing synthetic biological pattern formation remains challenging due to underlying theoretical complexity as well as the difficulty of engineering multicellular networks biochemically. Here, we introduce a cell-in-the-loop approach where living cells interact through in silico signaling, establishing a new testbed to interrogate theoretical principles when internal cell dynamics are incorporated rather than modeled. We present an easy-to-use theoretical test to predict the emergence of contrasting patterns in gene expression among laterally inhibiting cells. Guided by the theory, we experimentally demonstrate spontaneous checkerboard patterning in an optogenetic setup, where cell-to-cell signaling is emulated with light inputs calculated in silico from real-time gene expression measurements. The scheme successfully produces spontaneous, persistent checkerboard patterns for systems of sixteen patches, in quantitative agreement with theoretical predictions. Our research highlights how tools from dynamical systems theory may inform our understanding of patterning, and illustrates the potential of cell-in-the-loop for engineering synthetic multicellular systems.
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Affiliation(s)
- Melinda Liu Perkins
- Department of Electrical Engineering, University of California, Berkeley, CA, USA.
| | - Dirk Benzinger
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Murat Arcak
- Department of Electrical Engineering, University of California, Berkeley, CA, USA
| | - Mustafa Khammash
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
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33
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Husna N, Gascoigne NRJ, Tey HL, Ng LG, Tan Y. Reprint of "Multi-modal image cytometry approach - From dynamic to whole organ imaging". Cell Immunol 2020; 350:104086. [PMID: 32169249 DOI: 10.1016/j.cellimm.2020.104086] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 06/18/2019] [Accepted: 06/18/2019] [Indexed: 12/13/2022]
Abstract
Optical imaging is a valuable tool to visualise biological processes in the context of the tissue. Each imaging modality provides the biologist with different types of information - cell dynamics and migration over time can be tracked with time-lapse imaging (e.g. intra-vital imaging); an overview of whole tissues can be acquired using optical clearing in conjunction with light sheet microscopy; finer details such as cellular morphology and fine nerve tortuosity can be imaged at higher resolution using the confocal microscope. Multi-modal imaging combined with image cytometry - a form of quantitative analysis of image datasets - provides an objective basis for comparing between sample groups. Here, we provide an overview of technical aspects to look out for in an image cytometry workflow, and discuss issues related to sample preparation, image post-processing and analysis for intra-vital and whole organ imaging.
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Affiliation(s)
- Nazihah Husna
- Singapore Immunology Network (SIgN), A*STAR (Agency for Science, Technology and Research), Biopolis, 8A Biomedical Grove, Singapore 138648, Singapore; Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, 5 Science Drive 2, Singapore 117545, Singapore
| | - Nicholas R J Gascoigne
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, 5 Science Drive 2, Singapore 117545, Singapore
| | - Hong Liang Tey
- National Skin Centre, 1 Mandalay Road, Singapore 308205, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, 11 Mandalay Road, Singapore 308232, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Dr, Singapore 117597, Singapore
| | - Lai Guan Ng
- Singapore Immunology Network (SIgN), A*STAR (Agency for Science, Technology and Research), Biopolis, 8A Biomedical Grove, Singapore 138648, Singapore; Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, 5 Science Drive 2, Singapore 117545, Singapore.
| | - Yingrou Tan
- Singapore Immunology Network (SIgN), A*STAR (Agency for Science, Technology and Research), Biopolis, 8A Biomedical Grove, Singapore 138648, Singapore; National Skin Centre, 1 Mandalay Road, Singapore 308205, Singapore.
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Husna N, Gascoigne NR, Tey HL, Ng LG, Tan Y. Multi-modal image cytometry approach – From dynamic to whole organ imaging. Cell Immunol 2019; 344:103946. [DOI: 10.1016/j.cellimm.2019.103946] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 06/18/2019] [Accepted: 06/18/2019] [Indexed: 12/27/2022]
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35
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Luessen DJ, Sun H, McGinnis MM, Hagstrom M, Marrs G, McCool BA, Chen R. Acute ethanol exposure reduces serotonin receptor 1A internalization by increasing ubiquitination and degradation of β-arrestin2. J Biol Chem 2019; 294:14068-14080. [PMID: 31366729 DOI: 10.1074/jbc.ra118.006583] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Revised: 07/23/2019] [Indexed: 11/06/2022] Open
Abstract
Acute alcohol exposure alters the trafficking and function of many G-protein-coupled receptors (GPCRs) that are associated with aberrant behavioral responses to alcohol. However, the molecular mechanisms underlying alcohol-induced changes in GPCR function remain unclear. β-Arrestin is a key player involved in the regulation of GPCR internalization and thus controls the magnitude and duration of GPCR signaling. Although β-arrestin levels are influenced by various drugs of abuse, the effect of alcohol exposure on β-arrestin expression and β-arrestin-mediated GPCR trafficking is poorly understood. Here, we found that acute ethanol exposure increases β-arrestin2 degradation via its increased ubiquitination in neuroblastoma-2a (N2A) cells and rat prefrontal cortex (PFC). β-Arrestin2 ubiquitination was likely mediated by the E3 ligase MDM2 homolog (MDM2), indicated by an increased coupling between β-arrestin2 and MDM2 in response to acute ethanol exposure in both N2A cells and rat PFC homogenates. Importantly, ethanol-induced β-arrestin2 reduction was reversed by siRNA-mediated MDM2 knockdown or proteasome inhibition in N2A cells, suggesting β-arrestin2 degradation is mediated by MDM2 through the proteasomal pathway. Using serotonin 5-HT1A receptors (5-HT1ARs) as a model receptor system, we found that ethanol dose-dependently inhibits 5-HT1AR internalization and that MDM2 knockdown reverses this effect. Moreover, ethanol both reduced β-arrestin2 levels and delayed agonist-induced β-arrestin2 recruitment to the membrane. We conclude that β-arrestin2 dysregulation by ethanol impairs 5-HT1AR trafficking. Our findings reveal a critical molecular mechanism underlying ethanol-induced alterations in GPCR internalization and implicate β-arrestin as a potential player mediating behavioral responses to acute alcohol exposure.
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Affiliation(s)
- Deborah J Luessen
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston Salem, North Carolina 27157
| | - Haiguo Sun
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston Salem, North Carolina 27157
| | - Molly M McGinnis
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston Salem, North Carolina 27157
| | - Michael Hagstrom
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston Salem, North Carolina 27157
| | - Glen Marrs
- Center for Molecular Signaling, Department of Biology, Wake Forest University, Winston Salem, North Carolina 27106
| | - Brian A McCool
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston Salem, North Carolina 27157
| | - Rong Chen
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston Salem, North Carolina 27157 .,Center for Molecular Signaling, Department of Biology, Wake Forest University, Winston Salem, North Carolina 27106.,Center for the Neurobiology of Addiction Treatment, Wake Forest School of Medicine, Winston Salem, North Carolina 27157
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36
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Yu JG, Li Y, Gao C, Gaoa H, Xia GS, Yub ZL, Lic Y. Exemplar-Based Recursive Instance Segmentation With Application to Plant Image Analysis. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2019; 29:389-404. [PMID: 31329554 DOI: 10.1109/tip.2019.2923571] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Instance segmentation is a challenging computer vision problem which lies at the intersection of object detection and semantic segmentation. Motivated by plant image analysis in the context of plant phenotyping, a recently emerging application field of computer vision, this paper presents the Exemplar-Based Recursive Instance Segmentation (ERIS) framework. A three-layer probabilistic model is firstly introduced to jointly represent hypotheses, voting elements, instance labels and their connections. Afterwards, a recursive optimization algorithm is developed to infer the maximum a posteriori (MAP) solution, which handles one instance at a time by alternating among the three steps of detection, segmentation and update. The proposed ERIS framework departs from previous works mainly in two respects. First, it is exemplar-based and model-free, which can achieve instance-level segmentation of a specific object class given only a handful of (typically less than 10) annotated exemplars. Such a merit enables its use in case that no massive manually-labeled data is available for training strong classification models, as required by most existing methods. Second, instead of attempting to infer the solution in a single shot, which suffers from extremely high computational complexity, our recursive optimization strategy allows for reasonably efficient MAP-inference in full hypothesis space. The ERIS framework is substantialized for the specific application of plant leaf segmentation in this work. Experiments are conducted on public benchmarks to demonstrate the superiority of our method in both effectiveness and efficiency in comparison with the state-of-the-art.
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37
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Vicar T, Balvan J, Jaros J, Jug F, Kolar R, Masarik M, Gumulec J. Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison. BMC Bioinformatics 2019; 20:360. [PMID: 31253078 PMCID: PMC6599268 DOI: 10.1186/s12859-019-2880-8] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 05/07/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Because of its non-destructive nature, label-free imaging is an important strategy for studying biological processes. However, routine microscopic techniques like phase contrast or DIC suffer from shadow-cast artifacts making automatic segmentation challenging. The aim of this study was to compare the segmentation efficacy of published steps of segmentation work-flow (image reconstruction, foreground segmentation, cell detection (seed-point extraction) and cell (instance) segmentation) on a dataset of the same cells from multiple contrast microscopic modalities. RESULTS We built a collection of routines aimed at image segmentation of viable adherent cells grown on the culture dish acquired by phase contrast, differential interference contrast, Hoffman modulation contrast and quantitative phase imaging, and we performed a comprehensive comparison of available segmentation methods applicable for label-free data. We demonstrated that it is crucial to perform the image reconstruction step, enabling the use of segmentation methods originally not applicable on label-free images. Further we compared foreground segmentation methods (thresholding, feature-extraction, level-set, graph-cut, learning-based), seed-point extraction methods (Laplacian of Gaussians, radial symmetry and distance transform, iterative radial voting, maximally stable extremal region and learning-based) and single cell segmentation methods. We validated suitable set of methods for each microscopy modality and published them online. CONCLUSIONS We demonstrate that image reconstruction step allows the use of segmentation methods not originally intended for label-free imaging. In addition to the comprehensive comparison of methods, raw and reconstructed annotated data and Matlab codes are provided.
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Affiliation(s)
- Tomas Vicar
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 3058/10, Brno, CZ-61600, Czech Republic.,Department of Physiology, Faculty of Medicine, Masaryk University, Kamenice 5, Brno, CZ-62500, Czech Republic
| | - Jan Balvan
- Department of Pathological Physiology, Faculty of Medicine, Masaryk University, Kamenice 5, Brno, CZ-62500, Czech Republic.,Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, Brno, CZ-612 00, Czech Republic
| | - Josef Jaros
- Department of Histology and Embryology, Faculty of Medicine, Masaryk University, Kamenice 5, Brno, CZ-62500, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital, Pekarska 664/53, Brno, CZ-65691, Czech Republic
| | - Florian Jug
- Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstr. 108, Dresden, DE-01307, Germany
| | - Radim Kolar
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 3058/10, Brno, CZ-61600, Czech Republic
| | - Michal Masarik
- Department of Pathological Physiology, Faculty of Medicine, Masaryk University, Kamenice 5, Brno, CZ-62500, Czech Republic.,Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, Brno, CZ-612 00, Czech Republic
| | - Jaromir Gumulec
- Department of Physiology, Faculty of Medicine, Masaryk University, Kamenice 5, Brno, CZ-62500, Czech Republic. .,Department of Pathological Physiology, Faculty of Medicine, Masaryk University, Kamenice 5, Brno, CZ-62500, Czech Republic. .,Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, Brno, CZ-612 00, Czech Republic.
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38
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Zhang H, Söderholm N, Sandblad L, Wiklund K, Andersson M. DSeg: A Dynamic Image Segmentation Program to Extract Backbone Patterns for Filamentous Bacteria and Hyphae Structures. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2019; 25:711-719. [PMID: 30894244 DOI: 10.1017/s1431927619000308] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Analysis of numerous filamentous structures in an image is often limited by the ability of algorithms to accurately segment complex structures or structures within a dense population. It is even more problematic if these structures continuously grow when recording a time-series of images. To overcome these issues we present DSeg; an image analysis program designed to process time-series image data, as well as single images, to segment filamentous structures. The program includes a robust binary level-set algorithm modified to use size constraints, edge intensity, and past information. We verify our algorithms using synthetic data, differential interference contrast images of filamentous prokaryotes, and transmission electron microscopy images of bacterial adhesion fimbriae. DSeg includes automatic segmentation, tools for analysis, and drift correction, and outputs statistical data such as persistence length, growth rate, and growth direction. The program is available at Sourceforge.
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Affiliation(s)
- Hanqing Zhang
- Department of Physics,Umeå University,901 87 Umeå,Sweden
| | - Niklas Söderholm
- Department of Molecular Biology,Umeå University,901 87 Umeå,Sweden
| | - Linda Sandblad
- Department of Molecular Biology,Umeå University,901 87 Umeå,Sweden
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39
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Henkel AW, Al-Abdullah LAAD, Al-Qallaf MS, Redzic ZB. Quantitative Determination of Cellular-and Neurite Motility Speed in Dense Cell Cultures. Front Neuroinform 2019; 13:15. [PMID: 30914941 PMCID: PMC6423175 DOI: 10.3389/fninf.2019.00015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 02/19/2019] [Indexed: 12/16/2022] Open
Abstract
Mobility quantification of single cells and cellular processes in dense cultures is a challenge, because single cell tracking is impossible. We developed a software for cell structure segmentation and implemented 2 algorithms to measure motility speed. Complex algorithms were tested to separate cells and cellular components, an important prerequisite for the acquisition of meaningful motility data. Plasma membrane segmentation was performed to measure membrane contraction dynamics and organelle trafficking. The discriminative performance and sensitivity of the algorithms were tested on different cell types and calibrated on computer-simulated cells to obtain absolute values for cellular velocity. Both motility algorithms had advantages in different experimental setups, depending on the complexity of the cellular movement. The correlation algorithm (COPRAMove) performed best under most tested conditions and appeared less sensitive to variable cell densities, brightness and focus changes than the differentiation algorithm (DiffMove). In summary, our software can be used successfully to analyze and quantify cellular and subcellular movements in dense cell cultures.
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Affiliation(s)
- Andreas W Henkel
- Department of Physiology, Faculty of Medicine, Kuwait University, Kuwait City, Kuwait
| | | | - Mohammed S Al-Qallaf
- Department of Physiology, Faculty of Medicine, Kuwait University, Kuwait City, Kuwait
| | - Zoran B Redzic
- Department of Physiology, Faculty of Medicine, Kuwait University, Kuwait City, Kuwait
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40
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Abdolhoseini M, Kluge MG, Walker FR, Johnson SJ. Segmentation of Heavily Clustered Nuclei from Histopathological Images. Sci Rep 2019; 9:4551. [PMID: 30872619 PMCID: PMC6418222 DOI: 10.1038/s41598-019-38813-2] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Accepted: 12/10/2018] [Indexed: 01/27/2023] Open
Abstract
Automated cell nucleus segmentation is the key to gain further insight into cell features and functionality which support computer-aided pathology in early diagnosis of diseases such as breast cancer and brain tumour. Despite considerable advances in automated segmentation, it still remains a challenging task to split heavily clustered nuclei due to intensity variations caused by noise and uneven absorption of stains. To address this problem, we propose a novel method applicable to variety of histopathological images stained for different proteins, with high speed, accuracy and level of automation. Our algorithm is initiated by applying a new locally adaptive thresholding method on watershed regions. Followed by a new splitting technique based on multilevel thresholding and the watershed algorithm to separate clustered nuclei. Finalized by a model-based merging step to eliminate oversegmentation and a model-based correction step to improve segmentation results and eliminate small objects. We have applied our method to three image datasets: breast cancer stained for hematoxylin and eosin (H&E), Drosophila Kc167 cells stained for DNA to label nuclei, and mature neurons stained for NeuN. Evaluated results show our method outperforms the state-of-the-art methods in terms of accuracy, precision, F1-measure, and computational time.
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Affiliation(s)
- Mahmoud Abdolhoseini
- The University of Newcastle, School of Electrical Engineering and Computing, Callaghan, NSW, 2308, Australia.
| | - Murielle G Kluge
- The University of Newcastle, School of Biomedical Sciences and Pharmacy, Callaghan, NSW, 2308, Australia.,The Hunter Medical Research Institute, New Lambton, NSW, 2305, Australia
| | - Frederick R Walker
- The University of Newcastle, School of Biomedical Sciences and Pharmacy, Callaghan, NSW, 2308, Australia.,The Hunter Medical Research Institute, New Lambton, NSW, 2305, Australia
| | - Sarah J Johnson
- The University of Newcastle, School of Electrical Engineering and Computing, Callaghan, NSW, 2308, Australia.,The Hunter Medical Research Institute, New Lambton, NSW, 2305, Australia
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41
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Rullan M, Benzinger D, Schmidt GW, Milias-Argeitis A, Khammash M. An Optogenetic Platform for Real-Time, Single-Cell Interrogation of Stochastic Transcriptional Regulation. Mol Cell 2019; 70:745-756.e6. [PMID: 29775585 PMCID: PMC5971206 DOI: 10.1016/j.molcel.2018.04.012] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2017] [Revised: 02/07/2018] [Accepted: 04/12/2018] [Indexed: 02/01/2023]
Abstract
Transcription is a highly regulated and inherently stochastic process. The complexity of signal transduction and gene regulation makes it challenging to analyze how the dynamic activity of transcriptional regulators affects stochastic transcription. By combining a fast-acting, photo-regulatable transcription factor with nascent RNA quantification in live cells and an experimental setup for precise spatiotemporal delivery of light inputs, we constructed a platform for the real-time, single-cell interrogation of transcription in Saccharomyces cerevisiae. We show that transcriptional activation and deactivation are fast and memoryless. By analyzing the temporal activity of individual cells, we found that transcription occurs in bursts, whose duration and timing are modulated by transcription factor activity. Using our platform, we regulated transcription via light-driven feedback loops at the single-cell level. Feedback markedly reduced cell-to-cell variability and led to qualitative differences in cellular transcriptional dynamics. Our platform establishes a flexible method for studying transcriptional dynamics in single cells.
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Affiliation(s)
- Marc Rullan
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, 4058 Basel-Stadt, Switzerland
| | - Dirk Benzinger
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, 4058 Basel-Stadt, Switzerland
| | - Gregor W Schmidt
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, 4058 Basel-Stadt, Switzerland
| | - Andreas Milias-Argeitis
- Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, 9747 AG Groningen, the Netherlands.
| | - Mustafa Khammash
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, 4058 Basel-Stadt, Switzerland.
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42
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Micro-Net: A unified model for segmentation of various objects in microscopy images. Med Image Anal 2019; 52:160-173. [DOI: 10.1016/j.media.2018.12.003] [Citation(s) in RCA: 89] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 12/13/2018] [Accepted: 12/14/2018] [Indexed: 11/23/2022]
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43
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A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics. Cell Syst 2019; 8:15-26.e11. [PMID: 30638813 DOI: 10.1016/j.cels.2018.12.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 10/16/2018] [Accepted: 12/11/2018] [Indexed: 01/26/2023]
Abstract
Single-cell time-lapse data provide the means for disentangling sources of cell-to-cell and intra-cellular variability, a key step for understanding heterogeneity in cell populations. However, single-cell analysis with dynamic models is a challenging open problem: current inference methods address only single-gene expression or neglect parameter correlations. We report on a simple, flexible, and scalable method for estimating cell-specific and population-average parameters of non-linear mixed-effects models of cellular networks, demonstrating its accuracy with a published model and dataset. We also propose sensitivity analysis for identifying which biological sub-processes quantitatively and dynamically contribute to cell-to-cell variability. Our application to endocytosis in yeast demonstrates that dynamic models of realistic size can be developed for the analysis of single-cell data and that shifting the focus from single reactions or parameters to nuanced and time-dependent contributions of sub-processes helps biological interpretation. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics.
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44
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Abstract
The ability to gain quantifiable, single-cell data from time-lapse microscopy images is dependent upon cell segmentation and tracking. Here, we present a detailed protocol for obtaining quality time-lapse movies and introduce a method to identify (segment) and track cells based on machine learning techniques (Fiji's Trainable Weka Segmentation) and custom, open-source Python scripts. To provide a hands-on experience, we provide datasets obtained using the aforementioned protocol.
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45
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Adolf F, Rhiel M, Hessling B, Gao Q, Hellwig A, Béthune J, Wieland FT. Proteomic Profiling of Mammalian COPII and COPI Vesicles. Cell Rep 2019; 26:250-265.e5. [DOI: 10.1016/j.celrep.2018.12.041] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 09/08/2018] [Accepted: 12/10/2018] [Indexed: 12/26/2022] Open
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46
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Song J, Xiao L, Lian Z. Contour-Seed Pairs Learning-Based Framework for Simultaneously Detecting and Segmenting Various Overlapping Cells/Nuclei in Microscopy Images. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2018; 27:5759-5774. [PMID: 30028701 DOI: 10.1109/tip.2018.2857001] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
In this paper, we propose a novel contour-seed pairs learning-based framework for robust and automated cell/nucleus segmentation. Automated granular object segmentation in microscopy images has significant clinical importance for pathology grading of the cell carcinoma and gene expression. The focus of the past literature is dominated by either segmenting a certain type of cells/nuclei or simply splitting the clustered objects without contours inference of them. Our method addresses these issues by formulating the detection and segmentation tasks in terms of a unified regression problem, where a cascade sparse regression chain model is trained and then applied to return object locations and entire boundaries of clustered objects. In particular, we first learn a set of online convolutional features in each layer. Then, in the proposed cascade sparse regression chain, with the input from the learned features, we iteratively update the locations and clustered object boundaries until convergence. In this way, the boundary evidences of each individual object can be easily delineated and be further fed to a complete contour inference procedure optimized by the minimum description length principle. For any probe image, our method enables to analyze free-lying and overlapping cells with complex shapes. Experimental results show that the proposed method is very generic and performs well on contour inferences of various cell/nucleus types. Compared with the current segmentation techniques, our approach achieves state-of-the-art performances on four challenging datasets, i.e., the kidney renal cell carcinoma histopathology dataset, Drosophila Kc167 cellular dataset, differential interference contrast red blood cell dataset, and cervical cytology dataset.
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47
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Bakker E, Swain PS, Crane MM. Morphologically constrained and data informed cell segmentation of budding yeast. Bioinformatics 2018; 34:88-96. [PMID: 28968663 DOI: 10.1093/bioinformatics/btx550] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 09/03/2017] [Indexed: 01/11/2023] Open
Abstract
Motivation Although high-content image cytometry is becoming increasingly routine, processing the large amount of data acquired during time-lapse experiments remains a challenge. The majority of approaches for automated single-cell segmentation focus on flat, uniform fields of view covered with a single layer of cells. In the increasingly popular microfluidic devices that trap individual cells for long term imaging, these conditions are not met. Consequently, most techniques for segmentation perform poorly. Although potentially constraining the generalizability of software, incorporating information about the microfluidic features, flow of media and the morphology of the cells can substantially improve performance. Results Here we present DISCO (Data Informed Segmentation of Cell Objects), a framework for using the physical constraints imposed by microfluidic traps, the shape based morphological constraints of budding yeast and temporal information about cell growth and motion to allow tracking and segmentation of cells in microfluidic devices. Using manually curated datasets, we demonstrate substantial improvements in both tracking and segmentation when compared with existing software. Availability and implementation The MATLAB code for the algorithm and for measuring performance is available at https://github.com/pswain/segmentation-software and the test images and the curated ground-truth results used for comparing the algorithms are available at http://datashare.is.ed.ac.uk/handle/10283/2002. Contact mcrane2@uw.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Elco Bakker
- SynthSys-Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3BF, UK.,School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK
| | - Peter S Swain
- SynthSys-Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3BF, UK.,School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK
| | - Matthew M Crane
- SynthSys-Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3BF, UK.,School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK
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48
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Investigating the Relevance of Graph Cut Parameter on Interactive and Automatic Cell Segmentation. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2018; 2018:7396910. [PMID: 30344617 PMCID: PMC6158953 DOI: 10.1155/2018/7396910] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 06/24/2018] [Accepted: 08/03/2018] [Indexed: 11/17/2022]
Abstract
Graph cut segmentation provides a platform to analyze images through a global segmentation strategy, and as a result of this, it has gained a wider acceptability in many interactive and automatic segmentation fields of application, such as the medical field. The graph cut energy function has a parameter that is tuned to ensure that the output is neither oversegmented (shrink bias) nor undersegmented. Models have been proposed in literature towards the improvement of graph cut segmentation, in the context of interactive and automatic cell segmentation. Along this line of research, the graph cut parameter has been leveraged, while in some instances, it has been ignored. Therefore, in this work, the relevance of graph cut parameter on both interactive and automatic cell segmentation is investigated. Statistical analysis, based on F1 score, of three publicly available datasets of cells, suggests that the graph cut parameter plays a significant role in improving the segmentation accuracy of the interactive graph cut than the automatic graph cut.
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49
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Identification of individual cells from z-stacks of bright-field microscopy images. Sci Rep 2018; 8:11455. [PMID: 30061662 PMCID: PMC6065389 DOI: 10.1038/s41598-018-29647-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Accepted: 07/16/2018] [Indexed: 12/25/2022] Open
Abstract
Obtaining single cell data from time-lapse microscopy images is critical for quantitative biology, but bottlenecks in cell identification and segmentation must be overcome. We propose a novel, versatile method that uses machine learning classifiers to identify cell morphologies from z-stack bright-field microscopy images. We show that axial information is enough to successfully classify the pixels of an image, without the need to consider in focus morphological features. This fast, robust method can be used to identify different cell morphologies, including the features of E. coli, S. cerevisiae and epithelial cells, even in mixed cultures. Our method demonstrates the potential of acquiring and processing Z-stacks for single-layer, single-cell imaging and segmentation.
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50
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Cerbino R, Cicuta P. Perspective: Differential dynamic microscopy extracts multi-scale activity in complex fluids and biological systems. J Chem Phys 2018; 147:110901. [PMID: 28938830 DOI: 10.1063/1.5001027] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Differential dynamic microscopy (DDM) is a technique that exploits optical microscopy to obtain local, multi-scale quantitative information about dynamic samples, in most cases without user intervention. It is proving extremely useful in understanding dynamics in liquid suspensions, soft materials, cells, and tissues. In DDM, image sequences are analyzed via a combination of image differences and spatial Fourier transforms to obtain information equivalent to that obtained by means of light scattering techniques. Compared to light scattering, DDM offers obvious advantages, principally (a) simplicity of the setup; (b) possibility of removing static contributions along the optical path; (c) power of simultaneous different microscopy contrast mechanisms; and (d) flexibility of choosing an analysis region, analogous to a scattering volume. For many questions, DDM has also advantages compared to segmentation/tracking approaches and to correlation techniques like particle image velocimetry. The very straightforward DDM approach, originally demonstrated with bright field microscopy of aqueous colloids, has lately been used to probe a variety of other complex fluids and biological systems with many different imaging methods, including dark-field, differential interference contrast, wide-field, light-sheet, and confocal microscopy. The number of adopting groups is rapidly increasing and so are the applications. Here, we briefly recall the working principles of DDM, we highlight its advantages and limitations, we outline recent experimental breakthroughs, and we provide a perspective on future challenges and directions. DDM can become a standard primary tool in every laboratory equipped with a microscope, at the very least as a first bias-free automated evaluation of the dynamics in a system.
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Affiliation(s)
- Roberto Cerbino
- Department of Medical Biotechnology and Translational Medicine, University of Milan, Segrate 20090, Italy
| | - Pietro Cicuta
- Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE, United Kingdom
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