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Gómez-de-Mariscal E, Grobe H, Pylvänäinen JW, Xénard L, Henriques R, Tinevez JY, Jacquemet G. CellTracksColab is a platform that enables compilation, analysis, and exploration of cell tracking data. PLoS Biol 2024; 22:e3002740. [PMID: 39116189 DOI: 10.1371/journal.pbio.3002740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 07/08/2024] [Indexed: 08/10/2024] Open
Abstract
In life sciences, tracking objects from movies enables researchers to quantify the behavior of single particles, organelles, bacteria, cells, and even whole animals. While numerous tools now allow automated tracking from video, a significant challenge persists in compiling, analyzing, and exploring the large datasets generated by these approaches. Here, we introduce CellTracksColab, a platform tailored to simplify the exploration and analysis of cell tracking data. CellTracksColab facilitates the compiling and analysis of results across multiple fields of view, conditions, and repeats, ensuring a holistic dataset overview. CellTracksColab also harnesses the power of high-dimensional data reduction and clustering, enabling researchers to identify distinct behavioral patterns and trends without bias. Finally, CellTracksColab also includes specialized analysis modules enabling spatial analyses (clustering, proximity to specific regions of interest). We demonstrate CellTracksColab capabilities with 3 use cases, including T cells and cancer cell migration, as well as filopodia dynamics. CellTracksColab is available for the broader scientific community at https://github.com/CellMigrationLab/CellTracksColab.
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Affiliation(s)
| | - Hanna Grobe
- Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, Turku, Finland
| | - Joanna W Pylvänäinen
- Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku and Åbo Akademi University, Turku, Finland
| | - Laura Xénard
- Institut Pasteur, Université Paris Cité, Image Analysis Hub, Paris, France
- Institut Pasteur, Université Paris Cité, INSERM UMR1225, Pathogenesis of Vascular Infections, Paris, France
| | - Ricardo Henriques
- Instituto Gulbenkian de Ciência, Oeiras, Portugal
- UCL Laboratory for Molecular Cell Biology, University College London, London, United Kingdom
| | - Jean-Yves Tinevez
- Institut Pasteur, Université Paris Cité, Image Analysis Hub, Paris, France
| | - Guillaume Jacquemet
- Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku and Åbo Akademi University, Turku, Finland
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- Turku Bioimaging, University of Turku and Åbo Akademi University, Turku, Finland
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2
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Lekkala S, Ren Y, Weeks J, Lee K, Tay AJH, Liu B, Xue T, Rainbolt J, Xie C, Schwarz EM, Yeh SCA. A semi-automated cell tracking protocol for quantitative analyses of neutrophil swarming to sterile and S. aureus contaminated bone implants in a mouse femur model. PLoS One 2024; 19:e0296140. [PMID: 38900759 PMCID: PMC11189170 DOI: 10.1371/journal.pone.0296140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 05/06/2024] [Indexed: 06/22/2024] Open
Abstract
Implant-associated osteomyelitis remains a major orthopaedic problem. As neutrophil swarming to the surgical site is a critical host response to prevent infection, visualization and quantification of this dynamic behavior at the native microenvironment of infection will elucidate previously unrecognized mechanisms central to understanding the host response. We recently developed longitudinal intravital imaging of the bone marrow (LIMB) to visualize host cells and fluorescent S. aureus on a contaminated transfemoral implant in live mice, which allows for direct visualization of bacteria colonization of the implant and host cellular responses using two-photon laser scanning microscopy. To the end of rigorous and reproducible quantitative outcomes of neutrophil swarming kinetics in this model, we developed a protocol for robust segmentation, tracking, and quantifications of neutrophil dynamics adapted from Trainable Weka Segmentation and TrackMate, two readily available Fiji/ImageJ plugins. In this work, Catchup mice with tdTomato expressing neutrophils received a transfemoral pin with or without ECFP/EGFP-expressing USA300 methicillin-resistant Staphylococcus aureus (MRSA) to obtain 30-minute LIMB videos at 2-, 4-, and 6-hours post-implantation. The developed semi-automated neutrophil tracking protocol was executed independently by two users to quantify the distance, displacement, speed, velocity, and directionality of the target cells. The results revealed high inter-user reliability for all outcomes (ICC > 0.96; p > 0.05). Consistent with the established paradigm on increased neutrophil swarming during active infection, the results also demonstrated increased neutrophil speed and velocity at all measured time points, and increased displacement at later time points (6 hours) in infected versus uninfected mice (p < 0.05). Neutrophils and bacteria also exhibit directionality during migration in the infected mice. The semi-automated cell tracking protocol provides a streamlined approach to robustly identify and track individual cells across diverse experimental settings and eliminates inter-observer variability.
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Affiliation(s)
- Sashank Lekkala
- Department of Biomedical Engineering, University of Rochester, Rochester, New York, United States of America
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Youliang Ren
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, New York, United States of America
- Department of Orthopaedics and Rehabilitation, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Jason Weeks
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Kevin Lee
- Department of Biomedical Engineering, University of Rochester, Rochester, New York, United States of America
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Allie Jia Hui Tay
- Department of Biomedical Engineering, University of Rochester, Rochester, New York, United States of America
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Bei Liu
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Thomas Xue
- Department of Biomedical Engineering, University of Rochester, Rochester, New York, United States of America
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Joshua Rainbolt
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Chao Xie
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, New York, United States of America
- Department of Orthopaedics and Rehabilitation, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Edward M. Schwarz
- Department of Biomedical Engineering, University of Rochester, Rochester, New York, United States of America
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, New York, United States of America
- Department of Orthopaedics and Rehabilitation, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Shu-Chi A. Yeh
- Department of Biomedical Engineering, University of Rochester, Rochester, New York, United States of America
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, New York, United States of America
- Department of Orthopaedics and Rehabilitation, University of Rochester Medical Center, Rochester, New York, United States of America
- Department of Physiology/Pharmacology, University of Rochester Medical Center, Rochester, New York, United States of America
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3
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McAfee L, Heath Z, Anderson W, Hozi M, Orr JW, Kang YA. The development of an automated microscope image tracking and analysis system. Biotechnol Prog 2024:e3490. [PMID: 38888043 DOI: 10.1002/btpr.3490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 05/29/2024] [Accepted: 06/09/2024] [Indexed: 06/20/2024]
Abstract
Microscopy image analysis plays a crucial role in understanding cellular behavior and uncovering important insights in various biological and medical research domains. Tracking cells within the time-lapse microscopy images is a fundamental technique that enables the study of cell dynamics, interactions, and migration. While manual cell tracking is possible, it is time-consuming and prone to subjective biases that impact results. In order to solve this issue, we sought to create an automated software solution, named cell analyzer, which is able to track cells within microscopy images with minimal input required from the user. The program of cell analyzer was written in Python utilizing the open source computer vision (OpenCV) library and featured a graphical user interface that makes it easy for users to access. The functions of all codes were verified through closeness, area, centroid, contrast, variance, and cell tracking test. Cell analyzer primarily utilizes image preprocessing and edge detection techniques to isolate cell boundaries for detection and analysis. It uniquely recorded the area, displacement, speed, size, and direction of detected cell objects and visualized the data collected automatically for fast analysis. Our cell analyzer provides an easy-to-use tool through a graphical user interface for tracking cell motion and analyzing quantitative cell images.
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Affiliation(s)
- Lillian McAfee
- Department of Mechanical, Civil, and Biomedical Engineering, George Fox University, Newberg, Oregon, USA
| | - Zach Heath
- Department of Computer science, George Fox University, Newberg, Oregon, USA
| | - William Anderson
- Department of Mechanical, Civil, and Biomedical Engineering, George Fox University, Newberg, Oregon, USA
| | - Marvin Hozi
- Department of Computer science, George Fox University, Newberg, Oregon, USA
| | - John Walker Orr
- Department of Computer science, George Fox University, Newberg, Oregon, USA
| | - Youngbok Abraham Kang
- Department of Mechanical, Civil, and Biomedical Engineering, George Fox University, Newberg, Oregon, USA
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4
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Toscano E, Cimmino E, Pennacchio FA, Riccio P, Poli A, Liu YJ, Maiuri P, Sepe L, Paolella G. Methods and computational tools to study eukaryotic cell migration in vitro. Front Cell Dev Biol 2024; 12:1385991. [PMID: 38887515 PMCID: PMC11180820 DOI: 10.3389/fcell.2024.1385991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 05/13/2024] [Indexed: 06/20/2024] Open
Abstract
Cellular movement is essential for many vital biological functions where it plays a pivotal role both at the single cell level, such as during division or differentiation, and at the macroscopic level within tissues, where coordinated migration is crucial for proper morphogenesis. It also has an impact on various pathological processes, one for all, cancer spreading. Cell migration is a complex phenomenon and diverse experimental methods have been developed aimed at dissecting and analysing its distinct facets independently. In parallel, corresponding analytical procedures and tools have been devised to gain deep insight and interpret experimental results. Here we review established experimental techniques designed to investigate specific aspects of cell migration and present a broad collection of historical as well as cutting-edge computational tools used in quantitative analysis of cell motion.
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Affiliation(s)
- Elvira Toscano
- Department of Molecular Medicine and Medical Biotechnology, Università Degli Studi di Napoli “Federico II”, Naples, Italy
- CEINGE Biotecnologie Avanzate Franco Salvatore, Naples, Italy
| | - Elena Cimmino
- Department of Molecular Medicine and Medical Biotechnology, Università Degli Studi di Napoli “Federico II”, Naples, Italy
| | - Fabrizio A. Pennacchio
- Laboratory of Applied Mechanobiology, Department of Health Sciences and Technology, Zurich, Switzerland
| | - Patrizia Riccio
- Department of Molecular Medicine and Medical Biotechnology, Università Degli Studi di Napoli “Federico II”, Naples, Italy
| | | | - Yan-Jun Liu
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Paolo Maiuri
- Department of Molecular Medicine and Medical Biotechnology, Università Degli Studi di Napoli “Federico II”, Naples, Italy
| | - Leandra Sepe
- Department of Molecular Medicine and Medical Biotechnology, Università Degli Studi di Napoli “Federico II”, Naples, Italy
| | - Giovanni Paolella
- Department of Molecular Medicine and Medical Biotechnology, Università Degli Studi di Napoli “Federico II”, Naples, Italy
- CEINGE Biotecnologie Avanzate Franco Salvatore, Naples, Italy
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5
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Goreke U, Gonzales A, Shipley B, Tincher M, Sharma O, Wulftange W, Man Y, An R, Hinczewski M, Gurkan UA. Motion Blur Microscopy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.08.561435. [PMID: 37873474 PMCID: PMC10592665 DOI: 10.1101/2023.10.08.561435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Imaging and characterizing the dynamics of cellular adhesion in blood samples is of fundamental importance in understanding biological function. In vitro microscopy methods are widely used for this task, but typically require diluting the blood with a buffer to allow for transmission of light. However whole blood provides crucial mechanical and chemical signaling cues that influence adhesion dynamics, which means that conventional approaches lack the full physiological complexity of living microvasculature. We propose to overcome this challenge by a new in vitro imaging method which we call motion blur microscopy (MBM). By decreasing the source light intensity and increasing the integration time during imaging, flowing cells are blurred, allowing us to identify adhered cells. Combined with an automated analysis using machine learning, we can for the first time reliably image cell interactions in microfluidic channels during whole blood flow. MBM provides a low cost, easy to implement alternative to intravital microscopy, the in vivo approach for studying how the whole blood environment shapes adhesion dynamics. We demonstrate the method's reproducibility and accuracy in two example systems where understanding cell interactions, adhesion, and motility is crucial-sickle red blood cells adhering to laminin, and CAR-T cells adhering to E-selectin. We illustrate the wide range of data types that can be extracted from this approach, including distributions of cell size and eccentricity, adhesion durations, trajectories and velocities of adhered cells moving on a functionalized surface, as well as correlations among these different features at the single cell level. In all cases MBM allows for rapid collection and processing of large data sets, ranging from thousands to hundreds of thousands of individual adhesion events. The method is generalizable to study adhesion mechanisms in a variety of diseases, including cancer, blood disorders, thrombosis, inflammatory and autoimmune diseases, as well as providing rich datasets for theoretical modeling of adhesion dynamics.
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Affiliation(s)
- Utku Goreke
- Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH
| | - Ayesha Gonzales
- Department of Physics, Case Western Reserve University, Cleveland, OH
| | - Brandon Shipley
- Department of Physics, Case Western Reserve University, Cleveland, OH
| | - Madeleine Tincher
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH
| | - Oshin Sharma
- Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH
| | - William Wulftange
- Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH
| | - Yuncheng Man
- Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH
| | - Ran An
- Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH
| | | | - Umut A. Gurkan
- Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH
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6
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Sugiyama H, Goto Y, Kondo Y, Coudreuse D, Aoki K. Live-cell imaging defines a threshold in CDK activity at the G2/M transition. Dev Cell 2024; 59:545-557.e4. [PMID: 38228139 DOI: 10.1016/j.devcel.2023.12.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 10/05/2023] [Accepted: 12/21/2023] [Indexed: 01/18/2024]
Abstract
Cyclin-dependent kinase (CDK) determines the temporal ordering of the cell cycle phases. However, despite significant progress in studying regulators of CDK and phosphorylation patterns of CDK substrates at the population level, it remains elusive how CDK regulators coordinately affect CDK activity at the single-cell level and how CDK controls the temporal order of cell cycle events. Here, we elucidate the dynamics of CDK activity in fission yeast and mammalian cells by developing a CDK activity biosensor, Eevee-spCDK. We find that although CDK activity does not necessarily correlate with cyclin levels, it converges to the same level around mitotic onset in several mutant backgrounds, including pom1Δ cells and wee1 or cdc25 overexpressing cells. These data provide direct evidence that cells enter the M phase when CDK activity reaches a high threshold, consistent with the quantitative model of cell cycle progression in fission yeast.
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Affiliation(s)
- Hironori Sugiyama
- Quantitative Biology Research Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan
| | - Yuhei Goto
- Quantitative Biology Research Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan; Division of Quantitative Biology, National Institute for Basic Biology, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan; Basic Biology Program, Graduate Institute for Advanced Studies, SOKENDAI, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan
| | - Yohei Kondo
- Quantitative Biology Research Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan; Division of Quantitative Biology, National Institute for Basic Biology, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan; Basic Biology Program, Graduate Institute for Advanced Studies, SOKENDAI, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan
| | - Damien Coudreuse
- Institute of Biochemistry and Cellular Genetics, UMR 5095, CNRS, Bordeaux University, 33077 Bordeaux, France
| | - Kazuhiro Aoki
- Quantitative Biology Research Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan; Division of Quantitative Biology, National Institute for Basic Biology, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan; Basic Biology Program, Graduate Institute for Advanced Studies, SOKENDAI, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan.
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7
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Cayuela López A, García-Cuesta EM, Gardeta SR, Rodríguez-Frade JM, Mellado M, Gómez-Pedrero JA, S. Sorzano CO. TrackAnalyzer: A Fiji/ImageJ toolbox for a holistic analysis of tracks. BIOLOGICAL IMAGING 2023; 3:e18. [PMID: 38510172 PMCID: PMC10951927 DOI: 10.1017/s2633903x23000181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 07/16/2023] [Accepted: 09/08/2023] [Indexed: 03/22/2024]
Abstract
Current live-cell imaging techniques make possible the observation of live events and the acquisition of large datasets to characterize the different parameters of the visualized events. They provide new insights into the dynamics of biological processes with unprecedented spatial and temporal resolutions. Here we describe the implementation and application of a new tool called TrackAnalyzer, accessible from Fiji and ImageJ. Our tool allows running semi-automated single-particle tracking (SPT) and subsequent motion classification, as well as quantitative analysis of diffusion and intensity for selected tracks relying on the graphical user interface (GUI) for large sets of temporal images (X-Y-T or X-Y-C-T dimensions). TrackAnalyzer also allows 3D visualization of the results as overlays of either spots, cells or end-tracks over time, along with corresponding feature extraction and further classification according to user criteria. Our analysis workflow automates the following steps: (1) spot or cell detection and filtering, (2) construction of tracks, (3) track classification and analysis (diffusion and chemotaxis), and (4) detailed analysis and visualization of all the outputs along the pipeline. All these analyses are automated and can be run in batch mode for a set of similar acquisitions.
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Affiliation(s)
- Ana Cayuela López
- Biocomputing Unit, National Centre for Biotechnology, Cantoblanco, Madrid, Spain
| | - Eva M. García-Cuesta
- Department of Immunology and Oncology, National Centre for Biotechnology, Cantoblanco, Madrid, Spain
| | - Sofía R. Gardeta
- Department of Immunology and Oncology, National Centre for Biotechnology, Cantoblanco, Madrid, Spain
| | | | - Mario Mellado
- Department of Immunology and Oncology, National Centre for Biotechnology, Cantoblanco, Madrid, Spain
| | - José Antonio Gómez-Pedrero
- Applied Optics Complutense Group, Faculty of Optics and Optometry, University Complutense of Madrid, Madrid, Spain
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8
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Corallo D, Dalla Vecchia M, Lazic D, Taschner-Mandl S, Biffi A, Aveic S. The molecular basis of tumor metastasis and current approaches to decode targeted migration-promoting events in pediatric neuroblastoma. Biochem Pharmacol 2023; 215:115696. [PMID: 37481138 DOI: 10.1016/j.bcp.2023.115696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 07/12/2023] [Accepted: 07/12/2023] [Indexed: 07/24/2023]
Abstract
Cell motility is a crucial biological process that plays a critical role in the development of multicellular organisms and is essential for tissue formation and regeneration. However, uncontrolled cell motility can lead to the development of various diseases, including neoplasms. In this review, we discuss recent advances in the discovery of regulatory mechanisms underlying the metastatic spread of neuroblastoma, a solid pediatric tumor that originates in the embryonic migratory cells of the neural crest. The highly motile phenotype of metastatic neuroblastoma cells requires targeting of intracellular and extracellular processes, that, if affected, would be helpful for the treatment of high-risk patients with neuroblastoma, for whom current therapies remain inadequate. Development of new potentially migration-inhibiting compounds and standardized preclinical approaches for the selection of anti-metastatic drugs in neuroblastoma will also be discussed.
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Affiliation(s)
- Diana Corallo
- Laboratory of Target Discovery and Biology of Neuroblastoma, Istituto di Ricerca Pediatrica (IRP), Fondazione Città della Speranza, 35127 Padova, Italy
| | - Marco Dalla Vecchia
- Laboratory of Target Discovery and Biology of Neuroblastoma, Istituto di Ricerca Pediatrica (IRP), Fondazione Città della Speranza, 35127 Padova, Italy
| | - Daria Lazic
- St. Anna Children's Cancer Research Institute, CCRI, Zimmermannplatz 10, 1090, Vienna, Austria
| | - Sabine Taschner-Mandl
- St. Anna Children's Cancer Research Institute, CCRI, Zimmermannplatz 10, 1090, Vienna, Austria
| | - Alessandra Biffi
- Pediatric Hematology, Oncology and Stem Cell Transplant Division, Woman's and Child Health Department, University of Padova, 35121 Padova, Italy
| | - Sanja Aveic
- Laboratory of Target Discovery and Biology of Neuroblastoma, Istituto di Ricerca Pediatrica (IRP), Fondazione Città della Speranza, 35127 Padova, Italy.
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9
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Shim C, Kim W, Nguyen TTD, Kim DY, Choi YS, Chung YD. CellTrackVis: interactive browser-based visualization for analyzing cell trajectories and lineages. BMC Bioinformatics 2023; 24:124. [PMID: 36991341 DOI: 10.1186/s12859-023-05218-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 02/28/2023] [Indexed: 03/31/2023] Open
Abstract
BACKGROUND Automatic cell tracking methods enable practitioners to analyze cell behaviors efficiently. Notwithstanding the continuous development of relevant software, user-friendly visualization tools have room for further improvements. Typical visualization mostly comes with main cell tracking tools as a simple plug-in, or relies on specific software/platforms. Although some tools are standalone, limited visual interactivity is provided, or otherwise cell tracking outputs are partially visualized. RESULTS This paper proposes a self-reliant visualization system, CellTrackVis, to support quick and easy analysis of cell behaviors. Interconnected views help users discover meaningful patterns of cell motions and divisions in common web browsers. Specifically, cell trajectory, lineage, and quantified information are respectively visualized in a coordinated interface. In particular, immediate interactions among modules enable the study of cell tracking outputs to be more effective, and also each component is highly customizable for various biological tasks. CONCLUSIONS CellTrackVis is a standalone browser-based visualization tool. Source codes and data sets are freely available at http://github.com/scbeom/celltrackvis with the tutorial at http://scbeom.github.io/ctv_tutorial .
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Affiliation(s)
- Changbeom Shim
- School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, Australia
| | - Wooil Kim
- Data Intelligence Team, Samsung Research, Seoul, South Korea
| | - Tran Thien Dat Nguyen
- School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, Australia
| | - Du Yong Kim
- School of Engineering, RMIT University, Melbourne, Australia
| | - Yu Suk Choi
- School of Human Sciences, University of Western Australia, Perth, Australia
| | - Yon Dohn Chung
- Department of Computer Science and Engineering, Korea University, Seoul, South Korea.
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10
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Park SA, Sipka T, Krivá Z, Lutfalla G, Nguyen-Chi M, Mikula K. Segmentation-based tracking of macrophages in 2D+time microscopy movies inside a living animal. Comput Biol Med 2023; 153:106499. [PMID: 36599208 DOI: 10.1016/j.compbiomed.2022.106499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/19/2022] [Accepted: 12/27/2022] [Indexed: 12/31/2022]
Abstract
The automated segmentation and tracking of macrophages during their migration are challenging tasks due to their dynamically changing shapes and motions. This paper proposes a new algorithm to achieve automatic cell tracking in time-lapse microscopy macrophage data. First, we design a segmentation method employing space-time filtering, local Otsu's thresholding, and the SUBSURF (subjective surface segmentation) method. Next, the partial trajectories for cells overlapping in the temporal direction are extracted in the segmented images. Finally, the extracted trajectories are linked by considering their direction of movement. The segmented images and the obtained trajectories from the proposed method are compared with those of the semi-automatic segmentation and manual tracking. The proposed tracking achieved 97.4% of accuracy for macrophage data under challenging situations, feeble fluorescent intensity, irregular shapes, and motion of macrophages. We expect that the automatically extracted trajectories of macrophages can provide pieces of evidence of how macrophages migrate depending on their polarization modes in the situation, such as during wound healing.
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Affiliation(s)
- Seol Ah Park
- Department of Mathematics and Descriptive Geometry, Slovak University of Technology in Bratislava, Radlinskeho 11, Bratislava, 810 05, Slovakia.
| | - Tamara Sipka
- LPHI Laboratory of Pathogen Host Interaction, CNRS, Univ. Montpellier, Place E.Bataillon-Building 24, 34095, Montpellier Cedex 05, France.
| | - Zuzana Krivá
- Department of Mathematics and Descriptive Geometry, Slovak University of Technology in Bratislava, Radlinskeho 11, Bratislava, 810 05, Slovakia.
| | - Georges Lutfalla
- LPHI Laboratory of Pathogen Host Interaction, CNRS, Univ. Montpellier, Place E.Bataillon-Building 24, 34095, Montpellier Cedex 05, France.
| | - Mai Nguyen-Chi
- LPHI Laboratory of Pathogen Host Interaction, CNRS, Univ. Montpellier, Place E.Bataillon-Building 24, 34095, Montpellier Cedex 05, France.
| | - Karol Mikula
- Department of Mathematics and Descriptive Geometry, Slovak University of Technology in Bratislava, Radlinskeho 11, Bratislava, 810 05, Slovakia.
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