To investigate these images, it is essential to develop advanced analytical software, including high-precision segmentation methods for extracting information on the position and morphology of individual cells, and tracking methods for quantifying the dynamics of cells, among other features. Recent advances in microscope technology have made this possible, but it is still difficult to analyze the enormous amount of image data obtained after image acquisition with high accuracy. For image analysis with high reproducibility, an image with a high S/N ratio is required, but, in many cases, it is difficult to obtain an image with a high S/N ratio at a high sampling rate because of phototoxicity in living cells or individuals 6, 7. Time-lapse imaging requires imaging at sufficient frequency to track a population of cells with irregular positions and morphological changes over time.
There are currently technical limitations to long-term live-cell time-lapse imaging. To better understand the regulatory mechanisms of cell migration, it is essential to have a time-lapse imaging tool to observe the continuity of cell behavior and cell lineages over a long period of time. Ĭell migration plays important roles in various processes, including embryonic development, cell differentiation, immune response, regeneration, and tumor invasion 1, 2, 3, 4, 5. LIM Tracker is implemented as a plugin for ImageJ/Fiji.
We present a tracking case study based on fluorescence microscopy images (NRK-52E/EKAREV-NLS cells or MCF-10A/H2B-iRFP-P2A-mScarlet-I-hGem-P2A-PIP-NLS-mNeonGreen cells) and phase contrast microscopy images (Glioblastoma-astrocytoma U373 cells). LIM Tracker allows researchers to track living objects with good usability and high versatility for various targets. Moreover, recognition functions with deep learning (DL) are also available, which can be used for a wide range of targets including stain-free images. In addition, the system incorporates a highly interactive and interlocking data visualization method, which displays analysis result in real time, making it possible to flexibly correct the data and reduce the burden of tracking work. LIM Tracker enables the seamless use of these functions. This software has a conventional tracking function consisting of recognition processing and link processing, a sequential search-type tracking function based on pattern matching, and a manual tracking function. In this paper, we introduce our cell tracking software “LIM Tracker”. However, the accompanying graphical user interfaces are often difficult to use and do not incorporate seamless manual correction, data analysis tools, or simple training set design tools if it is machine learning based. Finally, we validate the use of TrackMate for quantitative lifetime analysis of clathrin-mediated endocytosis in plant cells.Cell tracking is one of the most critical tools for time-lapse image analysis to observe cell behavior and cell lineages over a long period of time.
Second, we investigate the recruitment of NEMO (NF-κB essential modulator) clusters in fibroblasts after stimulation by the cytokine IL-1 and show that photodamage can generate artifacts in the shape of TrackMate characterized movements that confuse motility analysis. Our TrackMate-based lineage analysis indicates the lack of a cell-specific light-sensitive mechanism. First, we perform Caenorhabditis-elegans lineage analysis to assess how light-induced damage during imaging impairs its early development. The current capabilities of TrackMate are presented in the context of three different biological problems. This evolving framework provides researchers with the opportunity to quickly develop and optimize new algorithms based on existing TrackMate modules without the need of having to write de novo user interfaces, including visualization, analysis and exporting tools. TrackMate is an extensible platform where developers can easily write their own detection, particle linking, visualization or analysis algorithms within the TrackMate environment. The utility of TrackMate is further enhanced through its ability to be readily customized to meet specific tracking problems. TrackMate provides several visualization and analysis tools that aid in assessing the relevance of results. It is also easily scriptable and adaptable, operating equally well on 1D over time, 2D over time, 3D over time, or other single and multi-channel image variants. It offers a versatile and modular solution that works out of the box for end users, through a simple and intuitive user interface. We present TrackMate, an open source Fiji plugin for the automated, semi-automated, and manual tracking of single-particles.
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