1
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Pham DL, Gillette AA, Riendeau J, Wiech K, Guzman EC, Datta R, Skala MC. Perspectives on label-free microscopy of heterogeneous and dynamic biological systems. JOURNAL OF BIOMEDICAL OPTICS 2025; 29:S22702. [PMID: 38434231 PMCID: PMC10903072 DOI: 10.1117/1.jbo.29.s2.s22702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 11/22/2023] [Accepted: 12/14/2023] [Indexed: 03/05/2024]
Abstract
Significance Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging). Aim This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs. Approach Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended. Conclusion Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.
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Affiliation(s)
- Dan L. Pham
- University of Wisconsin—Madison, Department of Biomedical Engineering, Madison, Wisconsin, United States
| | | | | | - Kasia Wiech
- University of Wisconsin—Madison, Department of Biomedical Engineering, Madison, Wisconsin, United States
| | | | - Rupsa Datta
- Morgridge Institute for Research, Madison, Wisconsin, United States
| | - Melissa C. Skala
- University of Wisconsin—Madison, Department of Biomedical Engineering, Madison, Wisconsin, United States
- Morgridge Institute for Research, Madison, Wisconsin, United States
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2
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Doğru D, Özdemir GD, Özdemir MA, Ercan UK, Topaloğlu Avşar N, Güren O. An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology. BMC Med Imaging 2024; 24:158. [PMID: 38914942 PMCID: PMC11197287 DOI: 10.1186/s12880-024-01332-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 06/13/2024] [Indexed: 06/26/2024] Open
Abstract
BACKGROUND The assessment of in vitro wound healing images is critical for determining the efficacy of the therapy-of-interest that may influence the wound healing process. Existing methods suffer significant limitations, such as user dependency, time-consuming nature, and lack of sensitivity, thus paving the way for automated analysis approaches. METHODS Hereby, three structurally different variations of U-net architectures based on convolutional neural networks (CNN) were implemented for the segmentation of in vitro wound healing microscopy images. The developed models were fed using two independent datasets after applying a novel augmentation method aimed at the more sensitive analysis of edges after the preprocessing. Then, predicted masks were utilized for the accurate calculation of wound areas. Eventually, the therapy efficacy-indicator wound areas were thoroughly compared with current well-known tools such as ImageJ and TScratch. RESULTS The average dice similarity coefficient (DSC) scores were obtained as 0.958 ∼ 0.968 for U-net-based deep learning models. The averaged absolute percentage errors (PE) of predicted wound areas to ground truth were 6.41%, 3.70%, and 3.73%, respectively for U-net, U-net++, and Attention U-net, while ImageJ and TScratch had considerable averaged error rates of 22.59% and 33.88%, respectively. CONCLUSIONS Comparative analyses revealed that the developed models outperformed the conventional approaches in terms of analysis time and segmentation sensitivity. The developed models also hold great promise for the prediction of the in vitro wound area, regardless of the therapy-of-interest, cell line, magnification of the microscope, or other application-dependent parameters.
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Affiliation(s)
- Dilan Doğru
- Department of Biomedical Engineering, Graduate School of Natural and Applied Sciences, Izmir Katip Celebi University, Izmir, Turkey
| | - Gizem D Özdemir
- Department of Biomedical Engineering, Graduate School of Natural and Applied Sciences, Izmir Katip Celebi University, Izmir, Turkey
- Department of Biomedical Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, Izmir, Turkey
| | - Mehmet A Özdemir
- Department of Biomedical Engineering, Graduate School of Natural and Applied Sciences, Izmir Katip Celebi University, Izmir, Turkey.
- Department of Biomedical Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, Izmir, Turkey.
| | - Utku K Ercan
- Department of Biomedical Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, Izmir, Turkey
| | - Nermin Topaloğlu Avşar
- Department of Biomedical Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, Izmir, Turkey
| | - Onan Güren
- Department of Biomedical Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, Izmir, Turkey.
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3
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Sinitca AM, Kayumov AR, Zelenikhin PV, Porfiriev AG, Kaplun DI, Bogachev MI. Segmentation of patchy areas in biomedical images based on local edge density estimation. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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4
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Qureshi MH, Ozlu N, Bayraktar H. Adaptive tracking algorithm for trajectory analysis of cells and layer-by-layer assessment of motility dynamics. Comput Biol Med 2022; 150:106193. [PMID: 37859286 DOI: 10.1016/j.compbiomed.2022.106193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 09/26/2022] [Accepted: 10/08/2022] [Indexed: 11/03/2022]
Abstract
Tracking biological objects such as cells or subcellular components imaged with time-lapse microscopy enables us to understand the molecular principles about the dynamics of cell behaviors. However, automatic object detection, segmentation and extracting trajectories remain as a rate-limiting step due to intrinsic challenges of video processing. This paper presents an adaptive tracking algorithm (Adtari) that automatically finds the optimum search radius and cell linkages to determine trajectories in consecutive frames. A critical assumption in most tracking studies is that displacement remains unchanged throughout the movie and cells in a few frames are usually analyzed to determine its magnitude. Tracking errors and inaccurate association of cells may occur if the user does not correctly evaluate the value or prior knowledge is not present on cell movement. The key novelty of our method is that minimum intercellular distance and maximum displacement of cells between frames are dynamically computed and used to determine the threshold distance. Since the space between cells is highly variable in a given frame, our software recursively alters the magnitude to determine all plausible matches in the trajectory analysis. Our method therefore eliminates a major preprocessing step where a constant distance was used to determine the neighbor cells in tracking methods. Cells having multiple overlaps and splitting events were further evaluated by using the shape attributes including perimeter, area, ellipticity and distance. The features were applied to determine the closest matches by minimizing the difference in their magnitudes. Finally, reporting section of our software were used to generate instant maps by overlaying cell features and trajectories. Adtari was validated by using videos with variable signal-to-noise, contrast ratio and cell density. We compared the adaptive tracking with constant distance and other methods to evaluate performance and its efficiency. Our algorithm yields reduced mismatch ratio, increased ratio of whole cell track, higher frame tracking efficiency and allows layer-by-layer assessment of motility to characterize single-cells. Adaptive tracking provides a reliable, accurate, time efficient and user-friendly open source software that is well suited for analysis of 2D fluorescence microscopy video datasets.
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Affiliation(s)
- Mohammad Haroon Qureshi
- Department of Molecular Biology and Genetics, Koç University, Rumelifeneri Yolu, Sariyer, 34450, Istanbul, Turkey; Center for Translational Research, Koç University, Rumelifeneri Yolu, Sariyer, 34450, Istanbul, Turkey
| | - Nurhan Ozlu
- Department of Molecular Biology and Genetics, Koç University, Rumelifeneri Yolu, Sariyer, 34450, Istanbul, Turkey
| | - Halil Bayraktar
- Department of Molecular Biology and Genetics, Istanbul Technical University, Maslak, Sariyer, 34467, Istanbul, Turkey.
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5
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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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6
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Analysis of activation maps through global pooling measurements for texture classification. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2020.09.058] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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7
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Lee AJ, Hugonnet H, Park W, Park Y. Three-dimensional label-free imaging and quantification of migrating cells during wound healing. BIOMEDICAL OPTICS EXPRESS 2020; 11:6812-6824. [PMID: 33408962 PMCID: PMC7747906 DOI: 10.1364/boe.405087] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 10/12/2020] [Accepted: 10/12/2020] [Indexed: 05/25/2023]
Abstract
The wound-healing assay is a simple but effective tool for studying collective cell migration (CCM) that is widely used in biophysical studies and high-throughput screening. However, conventional imaging and analysis methods only address two-dimensional (2D) properties in a wound healing assay, such as gap closure rate. This is unfortunate because biological cells are complex 3D structures, and their dynamics provide significant information about cell physiology. Here, we presented 3D label-free imaging for wound healing assays and investigated the 3D dynamics of CCM using optical diffraction tomography. High-resolution subcellular structures as well as their collective dynamics were imaged and analyzed quantitatively.
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Affiliation(s)
- Ariel J. Lee
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | - Herve Hugonnet
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | - WeiSun Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, 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, KAIST, Daejeon, Republic of Korea
- Tomocube Inc., Daejeon, Republic of Korea
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8
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Um E, Oh JM, Granick S, Cho YK. Cell migration in microengineered tumor environments. LAB ON A CHIP 2017; 17:4171-4185. [PMID: 28971203 DOI: 10.1039/c7lc00555e] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Recent advances in microengineered cell migration platforms are discussed critically with a focus on how cell migration is influenced by engineered tumor microenvironments, the medical relevance being to understand how tumor microenvironments may promote or suppress the progression of cancer. We first introduce key findings in cancer cell migration under the influence of the physical environment, which is systematically controlled by microengineering technology, followed by multi-cues of physico-chemical factors, which represent the complexity of the tumor environment. Recognizing that cancer cells constantly communicate not only with each other but also with tumor-associated cells such as vascular, fibroblast, and immune cells, and also with non-cellular components, it follows that cell motility in tumor microenvironments, especially metastasis via the invasion of cancer cells into the extracellular matrix and other tissues, is closely related to the malignancy of cancer-related mortality. Medical relevance of forefront research realized in microfabricated devices, such as single cell sorting based on the analysis of cell migration behavior, may assist personalized theragnostics based on the cell migration phenotype. Furthermore, we urge development of theory and numerical understanding of single or collective cell migration in microengineered platforms to gain new insights in cancer metastasis and in therapeutic strategies.
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Affiliation(s)
- Eujin Um
- Center for Soft and Living Matter, Institute for Basic Science (IBS), Ulsan 44919, Republic of Korea
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9
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Bedoya C, Cardona A, Galeano J, Cortés-Mancera F, Sandoz P, Zarzycki A. Accurate Region-of-Interest Recovery Improves the Measurement of the Cell Migration Rate in the In Vitro Wound Healing Assay. SLAS Technol 2017; 22:626-635. [PMID: 28692403 DOI: 10.1177/2472630317717436] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The wound healing assay is widely used for the quantitative analysis of highly regulated cellular events. In this essay, a wound is voluntarily produced on a confluent cell monolayer, and then the rate of wound reduction (WR) is characterized by processing images of the same regions of interest (ROIs) recorded at different time intervals. In this method, sharp-image ROI recovery is indispensable to compensate for displacements of the cell cultures due either to the exploration of multiple sites of the same culture or to transfers from the microscope stage to a cell incubator. ROI recovery is usually done manually and, despite a low-magnification microscope objective is generally used (10x), repositioning imperfections constitute a major source of errors detrimental to the WR measurement accuracy. We address this ROI recovery issue by using pseudoperiodic patterns fixed onto the cell culture dishes, allowing the easy localization of ROIs and the accurate quantification of positioning errors. The method is applied to a tumor-derived cell line, and the WR rates are measured by means of two different image processing software. Sharp ROI recovery based on the proposed method is found to improve significantly the accuracy of the WR measurement and the positioning under the microscope.
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Affiliation(s)
- Cesar Bedoya
- 1 Facultad de Ingenierías/Grupo de Investigación en Materiales Avanzados y Energía MatyEr/Línea Biomateriales y Electromedicina, Instituto Tecnológico Metropolitano ITM, Medellín, Antioquia, Colombia
| | - Andrés Cardona
- 2 Facultad de Ciencias Exactas y Aplicadas/Grupo de Investigación e Innovación Biomédica-GIB/Laboratorio de Ciencias Biomédicas, Instituto Tecnológico Metropolitano ITM, Medellín, Antioquia, Colombia
| | - July Galeano
- 1 Facultad de Ingenierías/Grupo de Investigación en Materiales Avanzados y Energía MatyEr/Línea Biomateriales y Electromedicina, Instituto Tecnológico Metropolitano ITM, Medellín, Antioquia, Colombia
| | - Fabián Cortés-Mancera
- 2 Facultad de Ciencias Exactas y Aplicadas/Grupo de Investigación e Innovación Biomédica-GIB/Laboratorio de Ciencias Biomédicas, Instituto Tecnológico Metropolitano ITM, Medellín, Antioquia, Colombia
| | - Patrick Sandoz
- 3 Department of Applied Mechanics, FEMTO-ST Institute, University Bourgogne Franche-Comté, CNRS/UFC/ENSMM/UTBM, Besançon, France
| | - Artur Zarzycki
- 4 Facultad de Ingenierías/Grupo de Investigación en Automática, Electrónica y Ciencias Computacionales/Línea Sistemas de Control y Robótica, Instituto Tecnológico Metropolitano ITM, Medellín, Antioquia, Colombia
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10
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Cardona A, Ariza-Jiménez L, Uribe D, Arroyave JC, Galeano J, Cortés-Mancera FM. Bio-EdIP: An automatic approach for in vitro cell confluence images quantification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 145:23-33. [PMID: 28552123 DOI: 10.1016/j.cmpb.2017.03.026] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Revised: 03/18/2017] [Accepted: 03/24/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVES Cell imaging is a widely-employed technique to analyze multiple biological processes. Therefore, simple, accurate and quantitative tools are needed to understand cellular events. For this purpose, Bio-EdIP was developed as a user-friendly tool to quantify confluence levels using cell culture images. METHODS The proposed algorithm combines a pre-processing step with subsequent stages that involve local processing techniques and a morphological reconstruction-based segmentation algorithm. Segmentation performance was assessed in three constructed image sets, comparing F-measure scores and AUC values (ROC analysis) for Bio-EdIP, its previous version and TScratch. Furthermore, segmentation results were compared with published algorithms using eight public benchmarks. RESULTS Bio-EdIP automatically segmented cell-free regions from images of in vitro cell culture. Based on mean F-measure scores and ROC analysis, Bio-EdIP conserved a high performance regardless of image characteristics of the constructed dataset, when compared with its previous version and TScratch. Although acquisition quality of the public dataset affected Bio-EdIP segmentation, performance was better in two out of eight public sets. CONCLUSIONS Bio-EdIP is a user-friendly interface, which is useful for the automatic analysis of confluence levels and cell growth processes using in vitro cell culture images. Here, we also presented new manually annotated data for algorithms evaluation.
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Affiliation(s)
- Andrés Cardona
- Grupo de Investigación e Innovación Biomédica (GI(2)B), Facultad de Ciencias Exactas y Aplicadas, Instituto Tecnológico Metropolitano (ITM), Medellín, Colombia.
| | - Leandro Ariza-Jiménez
- Grupo de Investigación en Modelado Matemático (GRIMMAT), Escuela de Ciencias, Universidad EAFIT, Medellín, Colombia.
| | - Diego Uribe
- Grupo de Investigación e Innovación Biomédica (GI(2)B), Facultad de Ciencias Exactas y Aplicadas, Instituto Tecnológico Metropolitano (ITM), Medellín, Colombia.
| | - Johanna C Arroyave
- Grupo de Investigación e Innovación Biomédica (GI(2)B), Facultad de Ciencias Exactas y Aplicadas, Instituto Tecnológico Metropolitano (ITM), Medellín, Colombia.
| | - July Galeano
- Grupo de Investigación en Materiales Avanzados y Energía (MATyER), Facultad de Ingenierías, Instituto Tecnológico Metropolitano (ITM), Medellín, Colombia.
| | - Fabian M Cortés-Mancera
- Grupo de Investigación e Innovación Biomédica (GI(2)B), Facultad de Ciencias Exactas y Aplicadas, Instituto Tecnológico Metropolitano (ITM), Medellín, Colombia.
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11
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Gan Z, Ding L, Burckhardt CJ, Lowery J, Zaritsky A, Sitterley K, Mota A, Costigliola N, Starker CG, Voytas DF, Tytell J, Goldman RD, Danuser G. Vimentin Intermediate Filaments Template Microtubule Networks to Enhance Persistence in Cell Polarity and Directed Migration. Cell Syst 2016; 3:252-263.e8. [PMID: 27667364 PMCID: PMC5055390 DOI: 10.1016/j.cels.2016.08.007] [Citation(s) in RCA: 127] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Revised: 05/01/2016] [Accepted: 08/05/2016] [Indexed: 10/24/2022]
Abstract
Increased expression of vimentin intermediate filaments (VIFs) enhances directed cell migration, but the mechanism behind VIFs' effect on motility is not understood. VIFs interact with microtubules, whose organization contributes to polarity maintenance in migrating cells. Here, we characterize the dynamic coordination of VIF and microtubule networks in wounded monolayers of retinal pigment epithelial cells. By genome editing, we fluorescently labeled endogenous vimentin and α-tubulin, and we developed computational image analysis to delineate architecture and interactions of the two networks. Our results show that VIFs assemble an ultrastructural copy of the previously polarized microtubule network. Because the VIF network is long-lived compared to the microtubule network, VIFs template future microtubule growth along previous microtubule tracks, thus providing a feedback mechanism that maintains cell polarity. VIF knockdown prevents cells from polarizing and migrating properly during wound healing. We suggest that VIFs' templating function establishes a memory in microtubule organization that enhances persistence in cell polarization in general and migration in particular.
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Affiliation(s)
- Zhuo Gan
- Department of Cell Biology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75235, USA
| | - Liya Ding
- Department of Cell Biology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75235, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Christoph J Burckhardt
- Department of Cell Biology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75235, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Jason Lowery
- Department of Cell and Molecular Biology, Feinberg School of Medicine, Northwestern University, Evanston, IL 60208, USA
| | - Assaf Zaritsky
- Department of Cell Biology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75235, USA
| | | | - Andressa Mota
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Nancy Costigliola
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Colby G Starker
- Department of Genetics, Cell Biology & Development and Center for Genome Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Daniel F Voytas
- Department of Genetics, Cell Biology & Development and Center for Genome Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Jessica Tytell
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Robert D Goldman
- Department of Cell and Molecular Biology, Feinberg School of Medicine, Northwestern University, Evanston, IL 60208, USA
| | - Gaudenz Danuser
- Department of Cell Biology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75235, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA.
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12
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Zaritsky A, Welf ES, Tseng YY, Angeles Rabadán M, Serra-Picamal X, Trepat X, Danuser G. Seeds of Locally Aligned Motion and Stress Coordinate a Collective Cell Migration. Biophys J 2016; 109:2492-2500. [PMID: 26682808 DOI: 10.1016/j.bpj.2015.11.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2015] [Revised: 10/14/2015] [Accepted: 11/03/2015] [Indexed: 11/29/2022] Open
Abstract
We find how collective migration emerges from mechanical information transfer between cells. Local alignment of cell velocity and mechanical stress orientation-a phenomenon dubbed "plithotaxis"-plays a crucial role in inducing coordinated migration. Leader cells at the monolayer edge better align velocity and stress to migrate faster toward the open space. Local seeds of enhanced motion then generate stress on neighboring cells to guide their migration. Stress-induced motion propagates into the monolayer as well as along the monolayer boundary to generate increasingly larger clusters of coordinately migrating cells that move faster with enhanced alignment of velocity and stress. Together, our analysis provides a model of long-range mechanical communication between cells, in which plithotaxis translates local mechanical fluctuations into globally collective migration of entire tissues.
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Affiliation(s)
- Assaf Zaritsky
- Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, Texas; Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas.
| | - Erik S Welf
- Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, Texas; Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Yun-Yu Tseng
- Cell Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - M Angeles Rabadán
- Cell Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Xavier Serra-Picamal
- Institute for Bioengineering of Catalonia, ICREA and University of Barcelona, Barcelona, Spain
| | - Xavier Trepat
- Institute for Bioengineering of Catalonia, ICREA and University of Barcelona, Barcelona, Spain
| | - Gaudenz Danuser
- Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, Texas; Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas.
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13
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Al-Mamun M, Ravenhill L, Srisukkham W, Hossain A, Fall C, Ellis V, Bass R. Effects of Noninhibitory Serpin Maspin on the Actin Cytoskeleton: A Quantitative Image Modeling Approach. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2016; 22:394-409. [PMID: 26906065 DOI: 10.1017/s1431927616000520] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Recent developments in quantitative image analysis allow us to interrogate confocal microscopy images to answer biological questions. Clumped and layered cell nuclei and cytoplasm in confocal images challenges the ability to identify subcellular compartments. To date, there is no perfect image analysis method to identify cytoskeletal changes in confocal images. Here, we present a multidisciplinary study where an image analysis model was developed to allow quantitative measurements of changes in the cytoskeleton of cells with different maspin exposure. Maspin, a noninhibitory serpin influences cell migration, adhesion, invasion, proliferation, and apoptosis in ways that are consistent with its identification as a tumor metastasis suppressor. Using different cell types, we tested the hypothesis that reduction in cell migration by maspin would be reflected in the architecture of the actin cytoskeleton. A hybrid marker-controlled watershed segmentation technique was used to segment the nuclei, cytoplasm, and ruffling regions before measuring cytoskeletal changes. This was informed by immunohistochemical staining of cells transfected stably or transiently with maspin proteins, or with added bioactive peptides or protein. Image analysis results showed that the effects of maspin were mirrored by effects on cell architecture, in a way that could be described quantitatively.
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Affiliation(s)
- Mohammed Al-Mamun
- 1Computational Intelligence Group, Faculty of Engineering and Environment,Northumbria University,Newcastle upon Tyne NE1 8ST,UK
| | - Lorna Ravenhill
- 3School of Biological Sciences,University of East Anglia,Norwich,Norfolk, NR4 7TJ,UK
| | - Worawut Srisukkham
- 1Computational Intelligence Group, Faculty of Engineering and Environment,Northumbria University,Newcastle upon Tyne NE1 8ST,UK
| | - Alamgir Hossain
- 1Computational Intelligence Group, Faculty of Engineering and Environment,Northumbria University,Newcastle upon Tyne NE1 8ST,UK
| | - Charles Fall
- 1Computational Intelligence Group, Faculty of Engineering and Environment,Northumbria University,Newcastle upon Tyne NE1 8ST,UK
| | - Vincent Ellis
- 3School of Biological Sciences,University of East Anglia,Norwich,Norfolk, NR4 7TJ,UK
| | - Rosemary Bass
- 5Department of Applied Sciences, Faculty of Health and Life Sciences,Northumbria University,Newcastle upon Tyne NE1 8ST,UK
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14
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Taking Aim at Moving Targets in Computational Cell Migration. Trends Cell Biol 2016; 26:88-110. [DOI: 10.1016/j.tcb.2015.09.003] [Citation(s) in RCA: 81] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Revised: 08/31/2015] [Accepted: 09/03/2015] [Indexed: 01/07/2023]
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15
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Stamm A, Reimers K, Strauß S, Vogt P, Scheper T, Pepelanova I. In vitro wound healing assays – state of the art. ACTA ACUST UNITED AC 2016. [DOI: 10.1515/bnm-2016-0002] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
AbstractWound healing is essential for the restoration of the barrier function of the skin. During this process, cells at the wound edges proliferate and migrate, leading to re-epithelialization of the wound surface. Wound healing assays are used to study the molecular mechanisms of wound repair, as well as in the investigation of potential therapeutics and treatments for improved healing. Numerous models of wound healing have been developed in recent years. In this review, we focus on in vitro assays, as they allow a fast, cost-efficient and ethical alternative to animal models. This paper gives a general overview of 2-dimensional (2D) cell monolayer assays by providing a description of injury methods, as well as an evaluation of each assay’s strengths and limitations. We include a section reviewing assays performed in 3-dimensional (3D) culture, which employ bioengineered skin models to capture complex wound healing mechanics like cell-matrix interactions and the interplay of different cell types in the healing process. Finally, we discuss in detail available software tools and algorithms for data analysis.
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16
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Garibay-Cerdenares OL, Hernández-Ramírez VI, Osorio-Trujillo JC, Gallardo-Rincón D, Talamás-Rohana P. Haptoglobin and CCR2 receptor expression in ovarian cancer cells that were exposed to ascitic fluid: exploring a new role of haptoglobin in the tumoral microenvironment. Cell Adh Migr 2015. [PMID: 26211665 PMCID: PMC4955374 DOI: 10.1080/19336918.2015.1035504] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Haptoglobin (Hp) is an acute-phase protein that is produced by the liver to capture the iron that is present in the blood circulation, thus avoiding its accumulation in the blood. Moreover, Hp has been detected in a wide variety of tissues, in which it performs various functions. In addition, this protein is considered a potential biomarker in many diseases, such as cancer, including ovarian carcinoma; however, its participation in the cancerous processes has not yet been determined. The objective of this work was to demonstrate the expression of Hp and its receptor CCR2 in the ovarian cancer cells and its possible involvement in the process of cell migration through changes in the rearrangement of the actin cytoskeleton using western blot and wound-healing assays and confirming by confocal microscopy. Ovarian cancer cells express both Hp and its receptor CCR2 but only after exposure to ascitic fluid, inducing moderated cell migration. However, when the cells are exposed to exogenous Hp, the expression of CCR2 is induced together with drastic changes in the actin cytoskeleton rearrangement. At the same time, Hp induced cell migration in a much more efficient manner than did ascitic fluid. These effects were blocked when the CCR2 synthetic antagonist RS102895 was used to pretreat the cells. These results suggest that Hp-induced changes in the cell morphology, actin cytoskeleton structure, and migration ability of tumor cells, is possibly “preparing” these cells for the potential induction of the metastatic phenotype.
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Affiliation(s)
- O L Garibay-Cerdenares
- a Departamento de Infectómica y Patogénesis Molecular , CINVESTAV, IPN.,c Present address: Unidad Académica de Ciencias Químico Biológicas; Universidad Autónoma de Guerrero (Cátedra CONACYT)
| | | | | | - D Gallardo-Rincón
- b Departamento de Oncología Médica ; Instituto Nacional de Cancerología , México, D.F
| | - P Talamás-Rohana
- a Departamento de Infectómica y Patogénesis Molecular , CINVESTAV, IPN
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17
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Das AM, Eggermont AMM, ten Hagen TLM. A ring barrier–based migration assay to assess cell migration in vitro. Nat Protoc 2015; 10:904-15. [DOI: 10.1038/nprot.2015.056] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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18
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Brandes S, Mokhtari Z, Essig F, Hünniger K, Kurzai O, Figge MT. Automated segmentation and tracking of non-rigid objects in time-lapse microscopy videos of polymorphonuclear neutrophils. Med Image Anal 2015; 20:34-51. [DOI: 10.1016/j.media.2014.10.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2014] [Revised: 09/28/2014] [Accepted: 10/11/2014] [Indexed: 11/30/2022]
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19
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Izraely S, Sagi-Assif O, Klein A, Meshel T, Ben-Menachem S, Zaritsky A, Ehrlich M, Prieto VG, Bar-Eli M, Pirker C, Berger W, Nahmias C, Couraud PO, Hoon DS, Witz IP. The metastatic microenvironment: Claudin-1 suppresses the malignant phenotype of melanoma brain metastasis. Int J Cancer 2014; 136:1296-307. [DOI: 10.1002/ijc.29090] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2013] [Accepted: 07/07/2014] [Indexed: 12/14/2022]
Affiliation(s)
- Sivan Izraely
- Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences; Tel Aviv University; Tel Aviv Israel
| | - Orit Sagi-Assif
- Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences; Tel Aviv University; Tel Aviv Israel
| | - Anat Klein
- Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences; Tel Aviv University; Tel Aviv Israel
| | - Tsipi Meshel
- Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences; Tel Aviv University; Tel Aviv Israel
| | - Shlomit Ben-Menachem
- Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences; Tel Aviv University; Tel Aviv Israel
| | - Assaf Zaritsky
- Blavatnik School of Computer Science; Tel Aviv University; Tel Aviv Israel
| | - Marcelo Ehrlich
- Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences; Tel Aviv University; Tel Aviv Israel
| | - Victor G. Prieto
- Department of Pathology; The University of Texas M.D. Anderson Cancer Center; Houston TX
| | - Menashe Bar-Eli
- Department of Cancer Biology; The University of Texas MD Anderson Cancer Center; Houston TX
| | - Christine Pirker
- Institute of Cancer Research, Department of Medicine I; Medical University Vienna; Vienna Austria
| | - Walter Berger
- Institute of Cancer Research, Department of Medicine I; Medical University Vienna; Vienna Austria
| | - Clara Nahmias
- Inserm, U1016, Institut Cochin; Paris France
- Cnrs, UMR8104; Paris France
- University Paris Descartes; UMR-S 1016, Paris France
| | - Pierre-Olivier Couraud
- Inserm, U1016, Institut Cochin; Paris France
- Cnrs, UMR8104; Paris France
- University Paris Descartes; UMR-S 1016, Paris France
| | - Dave S.B. Hoon
- Department of Molecular Oncology; John Wayne Cancer Institute, Saint John's Health Center; Santa Monica CA
| | - Isaac P. Witz
- Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences; Tel Aviv University; Tel Aviv Israel
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20
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Treloar KK, Simpson MJ, McElwain DLS, Baker RE. Are in vitro estimates of cell diffusivity and cell proliferation rate sensitive to assay geometry? J Theor Biol 2014; 356:71-84. [PMID: 24787651 DOI: 10.1016/j.jtbi.2014.04.026] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2013] [Revised: 03/06/2014] [Accepted: 04/18/2014] [Indexed: 11/25/2022]
Abstract
Cells respond to various biochemical and physical cues during wound-healing and tumour progression. in vitro assays used to study these processes are typically conducted in one particular geometry and it is unclear how the assay geometry affects the capacity of cell populations to spread, or whether the relevant mechanisms, such as cell motility and cell proliferation, are somehow sensitive to the geometry of the assay. In this work we use a circular barrier assay to characterise the spreading of cell populations in two different geometries. Assay 1 describes a tumour-like geometry where a cell population spreads outwards into an open space. Assay 2 describes a wound-like geometry where a cell population spreads inwards to close a void. We use a combination of discrete and continuum mathematical models and automated image processing methods to obtain independent estimates of the effective cell diffusivity, D, and the effective cell proliferation rate, λ. Using our parameterised mathematical model we confirm that our estimates of D and λ accurately predict the time-evolution of the location of the leading edge and the cell density profiles for both assay 1 and assay 2. Our work suggests that the effective cell diffusivity is up to 50% lower for assay 2 compared to assay 1, whereas the effective cell proliferation rate is up to 30% lower for assay 2 compared to assay 1.
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Affiliation(s)
- Katrina K Treloar
- Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, Australia; Tissue Repair and Regeneration Program, Institute of Health and Biomedical Innovation, QUT, Brisbane, Australia
| | - Matthew J Simpson
- Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, Australia; Tissue Repair and Regeneration Program, Institute of Health and Biomedical Innovation, QUT, Brisbane, Australia.
| | - D L Sean McElwain
- Tissue Repair and Regeneration Program, Institute of Health and Biomedical Innovation, QUT, Brisbane, Australia
| | - Ruth E Baker
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, United Kingdom
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21
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Mualla F, Scholl S, Sommerfeldt B, Maier A, Hornegger J. Automatic Cell Detection in Bright-Field Microscope Images Using SIFT, Random Forests, and Hierarchical Clustering. IEEE TRANSACTIONS ON MEDICAL IMAGING 2013; 32:2274-2286. [PMID: 24001988 DOI: 10.1109/tmi.2013.2280380] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
We present a novel machine learning-based system for unstained cell detection in bright-field microscope images. The system is fully automatic since it requires no manual parameter tuning. It is also highly invariant with respect to illumination conditions and to the size and orientation of cells. Images from two adherent cell lines and one suspension cell line were used in the evaluation for a total number of more than 3500 cells. Besides real images, simulated images were also used in the evaluation. The detection error was between approximately zero and 15.5% which is a significantly superior performance compared to baseline approaches.
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22
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Zaritsky A, Manor N, Wolf L, Ben-Jacob E, Tsarfaty I. Benchmark for multi-cellular segmentation of bright field microscopy images. BMC Bioinformatics 2013; 14:319. [PMID: 24195722 PMCID: PMC3826518 DOI: 10.1186/1471-2105-14-319] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2013] [Accepted: 10/29/2013] [Indexed: 11/28/2022] Open
Abstract
Background Multi-cellular segmentation of bright field microscopy images is an essential computational step when quantifying collective migration of cells in vitro. Despite the availability of various tools and algorithms, no publicly available benchmark has been proposed for evaluation and comparison between the different alternatives. Description A uniform framework is presented to benchmark algorithms for multi-cellular segmentation in bright field microscopy images. A freely available set of 171 manually segmented images from diverse origins was partitioned into 8 datasets and evaluated on three leading designated tools. Conclusions The presented benchmark resource for evaluating segmentation algorithms of bright field images is the first public annotated dataset for this purpose. This annotated dataset of diverse examples allows fair evaluations and comparisons of future segmentation methods. Scientists are encouraged to assess new algorithms on this benchmark, and to contribute additional annotated datasets.
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Affiliation(s)
- Assaf Zaritsky
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, 69978, Israel.
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23
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Topman G, Shoham N, Sharabani-Yosef O, Lin FH, Gefen A. A new technique for studying directional cell migration in a hydrogel-based three-dimensional matrix for tissue engineering model systems. Micron 2013; 51:9-12. [DOI: 10.1016/j.micron.2013.06.002] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2013] [Revised: 05/26/2013] [Accepted: 06/03/2013] [Indexed: 12/30/2022]
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24
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Treloar KK, Simpson MJ. Sensitivity of edge detection methods for quantifying cell migration assays. PLoS One 2013; 8:e67389. [PMID: 23826283 PMCID: PMC3691172 DOI: 10.1371/journal.pone.0067389] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2013] [Accepted: 05/19/2013] [Indexed: 12/27/2022] Open
Abstract
Quantitative imaging methods to analyze cell migration assays are not standardized. Here we present a suite of two-dimensional barrier assays describing the collective spreading of an initially-confined population of 3T3 fibroblast cells. To quantify the motility rate we apply two different automatic image detection methods to locate the position of the leading edge of the spreading population after , and hours. These results are compared with a manual edge detection method where we systematically vary the detection threshold. Our results indicate that the observed spreading rates are very sensitive to the choice of image analysis tools and we show that a standard measure of cell migration can vary by as much as 25% for the same experimental images depending on the details of the image analysis tools. Our results imply that it is very difficult, if not impossible, to meaningfully compare previously published measures of cell migration since previous results have been obtained using different image analysis techniques and the details of these techniques are not always reported. Using a mathematical model, we provide a physical interpretation of our edge detection results. The physical interpretation is important since edge detection algorithms alone do not specify any physical measure, or physical definition, of the leading edge of the spreading population. Our modeling indicates that variations in the image threshold parameter correspond to a consistent variation in the local cell density. This means that varying the threshold parameter is equivalent to varying the location of the leading edge in the range of approximately 1–5% of the maximum cell density.
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Affiliation(s)
- Katrina K. Treloar
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Institute of Health and Biomedical Innovation (IHBI), Queensland University of Technology, Brisbane, Queensland, Australia
| | - Matthew J. Simpson
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Institute of Health and Biomedical Innovation (IHBI), Queensland University of Technology, Brisbane, Queensland, Australia
- * E-mail:
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25
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Mueller JL, Harmany ZT, Mito JK, Kennedy SA, Kim Y, Dodd L, Geradts J, Kirsch DG, Willett RM, Brown JQ, Ramanujam N. Quantitative Segmentation of Fluorescence Microscopy Images of Heterogeneous Tissue: Application to the Detection of Residual Disease in Tumor Margins. PLoS One 2013; 8:e66198. [PMID: 23824589 PMCID: PMC3688889 DOI: 10.1371/journal.pone.0066198] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2012] [Accepted: 05/03/2013] [Indexed: 12/03/2022] Open
Abstract
Purpose To develop a robust tool for quantitative in situ pathology that allows visualization of heterogeneous tissue morphology and segmentation and quantification of image features. Materials and Methods Tissue excised from a genetically engineered mouse model of sarcoma was imaged using a subcellular resolution microendoscope after topical application of a fluorescent anatomical contrast agent: acriflavine. An algorithm based on sparse component analysis (SCA) and the circle transform (CT) was developed for image segmentation and quantification of distinct tissue types. The accuracy of our approach was quantified through simulations of tumor and muscle images. Specifically, tumor, muscle, and tumor+muscle tissue images were simulated because these tissue types were most commonly observed in sarcoma margins. Simulations were based on tissue characteristics observed in pathology slides. The potential clinical utility of our approach was evaluated by imaging excised margins and the tumor bed in a cohort of mice after surgical resection of sarcoma. Results Simulation experiments revealed that SCA+CT achieved the lowest errors for larger nuclear sizes and for higher contrast ratios (nuclei intensity/background intensity). For imaging of tumor margins, SCA+CT effectively isolated nuclei from tumor, muscle, adipose, and tumor+muscle tissue types. Differences in density were correctly identified with SCA+CT in a cohort of ex vivo and in vivo images, thus illustrating the diagnostic potential of our approach. Conclusion The combination of a subcellular-resolution microendoscope, acriflavine staining, and SCA+CT can be used to accurately isolate nuclei and quantify their density in anatomical images of heterogeneous tissue.
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Affiliation(s)
- Jenna L. Mueller
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
- * E-mail:
| | - Zachary T. Harmany
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, United States of America
| | - Jeffrey K. Mito
- Department of Pharmacology & Cancer Biology, Duke University School of Medicine, Durham, North Carolina, United States of America
| | - Stephanie A. Kennedy
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
| | - Yongbaek Kim
- Laboratory of Veterinary Clinical Pathology, College of Veterinary Medicine, Seoul National University, Seoul, South Korea
| | - Leslie Dodd
- Department of Pathology, University of North Carolina School of Medicine, Chapel Hill, North Carolina, United States of America
| | - Joseph Geradts
- Department of Pathology, Duke University Medical Center, Durham, North Carolina, United States of America
| | - David G. Kirsch
- Department of Pharmacology & Cancer Biology, Duke University School of Medicine, Durham, North Carolina, United States of America
- Department of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina, United States of America
| | - Rebecca M. Willett
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, United States of America
| | - J. Quincy Brown
- Department of Biomedical Engineering, Tulane University, New Orleans, Louisiana, United States of America
| | - Nimmi Ramanujam
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
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26
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Heindl A, Seewald AK, Thalhammer T, Bises G, Schepelmann M, Uhrova H, Dekan S, Mesteri I, Rogojanu R, Ellinger I. Automated REcognition of tissue-associated erythrocytes (ARETE)-a new tool in tissue cytometry. Cytometry A 2013; 83:363-74. [PMID: 23401225 DOI: 10.1002/cyto.a.22258] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2012] [Revised: 11/26/2012] [Accepted: 12/23/2012] [Indexed: 12/29/2022]
Abstract
Automated microscopic image analysis of immunofluorescence-stained targets on tissue sections is challenged by autofluorescent elements such as erythrocytes, which might interfere with target segmentation and quantification. Therefore, we developed an automated system (Automated REcognition of Tissue-associated Erythrocytes; ARETE) for in silico exclusion of erythrocytes. To detect erythrocytes in transmission images, a cascade of boosted decision trees of Haar-like features was trained on 8,640/4,000 areas (15 × 15 pixels) with/without erythrocytes from images of placental sections (4 µm). Ground truth data were generated on 28 transmission images. At least two human experts labelled the area covered by erythrocytes. For validation, output masks of human experts and ARETE were compared pixel-wise against a mask obtained from majority voting of human experts. F1 score, specificity, and Cohen's κ coefficients were calculated. To study the influence of erythrocyte-derived autofluorescence, we investigated the expression levels of a protein (receptor for advanced glycated end products; RAGE) in placenta and number of Ki-67-positive/cytokeratin 8-positive epithelial cells in colon sections. ARETE exhibited high sensitivity (99.87%) and specificity (99.81%) on a training-subset and processed transmission images (1,392 × 1,024 pixels) within 4 sec. ARETE and human expert's F1-scores were 0.55 versus 0.76, specificities 0.85 versus 0.92 and Cohen's κ coefficients 0.41 versus 0.68. A ranking of Cohen's κ coefficient by the scale of Fleiss certified "good agreement" between ARETE and the human experts. Applying ARETE, we demonstrated 4-14% false-positive RAGE-expression in placenta, and 18% falsely detected proliferative epithelial cells in colon, caused by erythrocyte-autofluorescence. ARETE is a fast system for in silico reduction of erythrocytes, which improves automated image analysis in research and diagnostic pathology.
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Affiliation(s)
- Andreas Heindl
- Department of Pathophysiology and Allergy Research, Medical University Vienna, Vienna, Austria
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27
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Automated velocity mapping of migrating cell populations (AVeMap). Nat Methods 2012; 9:1081-3. [PMID: 23064519 DOI: 10.1038/nmeth.2209] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2012] [Accepted: 09/17/2012] [Indexed: 11/08/2022]
Abstract
Characterizing the migration of a population of cells remains laborious and somewhat subjective. Advances in genetics and robotics allow researchers to perform many experiments in parallel, but analyzing the large sets of data remains a bottleneck. Here we describe a rapid, fully automated correlation-based method for cell migration analysis, compatible with standard video microscopy. This method allows for the computation of quantitative migration parameters via an extensive dynamic mapping of cell displacements.
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28
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Milde F, Franco D, Ferrari A, Kurtcuoglu V, Poulikakos D, Koumoutsakos P. Cell Image Velocimetry (CIV): boosting the automated quantification of cell migration in wound healing assays. Integr Biol (Camb) 2012; 4:1437-47. [DOI: 10.1039/c2ib20113e] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Affiliation(s)
- Florian Milde
- Computational Science and Engineering Laboratory, ETH Zürich, CH-8092, Switzerland
| | - Davide Franco
- Laboratory of Thermodynamics in Emerging Technologies, ETH Zürich, CH-8092, Switzerland
| | - Aldo Ferrari
- Laboratory of Thermodynamics in Emerging Technologies, ETH Zürich, CH-8092, Switzerland
| | | | - Dimos Poulikakos
- Laboratory of Thermodynamics in Emerging Technologies, ETH Zürich, CH-8092, Switzerland
| | - Petros Koumoutsakos
- Computational Science and Engineering Laboratory, ETH Zürich, CH-8092, Switzerland
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29
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Zaritsky A, Natan S, Ben-Jacob E, Tsarfaty I. Emergence of HGF/SF-induced coordinated cellular motility. PLoS One 2012; 7:e44671. [PMID: 22970283 PMCID: PMC3435317 DOI: 10.1371/journal.pone.0044671] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2012] [Accepted: 08/06/2012] [Indexed: 12/11/2022] Open
Abstract
Collective cell migration plays a major role in embryonic morphogenesis, tissue remodeling, wound repair and cancer invasion. Despite many decades of extensive investigations, only few analytical tools have been developed to enhance the biological understanding of this important phenomenon. Here we present a novel quantitative approach to analyze long term kinetics of bright field time-lapse wound healing. Fully-automated spatiotemporal measures and visualization of cells' motility and implicit morphology were proven to be sound, repetitive and highly informative compared to single-cell tracking analysis. We study cellular collective migration induced by tyrosine kinase-growth factor signaling (Met-Hepatocyte Growth Factor/Scatter Factor (HGF/SF)). Our quantitative approach is applied to demonstrate that collective migration of the adenocarcinoma cell lines is characterized by simple morpho-kinetics. HGF/SF induces complex morpho-kinetic coordinated collective migration: cells at the front move faster and are more spread than those further away from the wound edge. As the wound heals, distant cells gradually accelerate and enhance spread and elongation -resembling the epithelial to mesenchymal transition (EMT), and then the cells become more spread and maintain higher velocity than cells located closer to the wound. Finally, upon wound closure, front cells halt, shrink and round up (resembling mesenchymal to epithelial transition (MET) phenotype) while distant cells undergo the same process gradually. Met inhibition experiments further validate that Met signaling dramatically alters the morpho-kinetic dynamics of the healing wound. Machine-learning classification was applied to demonstrate the generalization of our findings, revealing even subtle changes in motility patterns induced by Met-inhibition. It is concluded that activation of Met-signaling induces an elaborated model in which cells lead a coordinated increased motility along with gradual differentiation-based collective cell motility dynamics. Our quantitative phenotypes may guide future investigation on the molecular and cellular mechanisms of tyrosine kinase-induced coordinate cell motility and morphogenesis in metastasis.
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Affiliation(s)
- Assaf Zaritsky
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Sari Natan
- Department of Clinical Microbiology and Immunology, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Eshel Ben-Jacob
- School of Physics and Astronomy, The Raymond and Beverly Sackler Faculty of Exact Sciences, Tel-Aviv University, Tel-Aviv, Israel
- Center for Theoretical Biological Physics, Rice University, Houston, Texas, United States of America
- Research & Development Unit Assaf Harofeh Medical Center, Zerifin, Israel
- School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel
| | - Ilan Tsarfaty
- Department of Clinical Microbiology and Immunology, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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30
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Loerke D, le Duc Q, Blonk I, Kerstens A, Spanjaard E, Machacek M, Danuser G, de Rooij J. Quantitative imaging of epithelial cell scattering identifies specific inhibitors of cell motility and cell-cell dissociation. Sci Signal 2012; 5:rs5. [PMID: 22763340 DOI: 10.1126/scisignal.2002677] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The scattering of cultured epithelial cells in response to hepatocyte growth factor (HGF) is a model system that recapitulates key features of metastatic cell behavior in vitro, including disruption of cell-cell adhesions and induction of cell migration. We have developed image analysis tools that do not require fluorescence tagging and that automatically track and characterize three aspects of scattering in live cells: increase in cell motility, loss of cell-cell adhesion, and spatial dispersion of cells (the redistribution of cells during scattering). We used these tools to screen a library of drugs, and we identified several efficient inhibitors of scattering, which we classified as selective inhibitors of either motility or loss of cell-cell adhesion, or as nonselective inhibitors. We validated the inhibitors and putative targets from this screen in two unrelated model cell lines. Using pharmacological treatments and RNA interference (RNAi), we found that nonsteroidal anti-inflammatory drugs inhibited cell-cell dissociation, that indirubins inhibited cell motility, and that cyclin-dependent kinase 1 and ribosomal S6 kinase were signaling intermediates in HGF-induced cell scattering. This assay is suitable for larger-scale screenings of chemical compounds or RNAi libraries.
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Affiliation(s)
- Dinah Loerke
- Department of Cell Biology, Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA
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Chaudhury B, Kramer K, Elozory D, Hernandez G, Goldgof D, Hall LO, Mouton PR. A novel algorithm for automated counting of stained cells on thick tissue sections. 2012 25TH IEEE INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS) 2012. [DOI: 10.1109/cbms.2012.6266296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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