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Prigent S, Valades-Cruz CA, Leconte L, Salamero J, Kervrann C. STracking: a free and open-source python library for particle tracking and analysis. Bioinformatics 2022; 38:3671-3673. [PMID: 35639941 DOI: 10.1093/bioinformatics/btac365] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/05/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022] Open
Abstract
SUMMARY Analysis of intra and extra cellular dynamic like vesicles transport involves particle tracking algorithms. The design of a particle tracking pipeline is a routine but tedious task. Therefore, particle dynamics analysis is often performed by combining several pieces of software (filtering, detection, tracking…) requiring many manual operations, and thus leading to poorly reproducible results. Given the new segmentation tools based on deep learning, modularity and interoperability between software have become essential in particle tracking algorithms. A good synergy between a particle detector and a tracker is of paramount importance. In addition, a user-friendly interface to control the quality of estimated trajectories is necessary. To address these issues, we developed STracking, a python library that allows combining algorithms into standardized particle tracking pipelines. AVAILABILITY AND IMPLEMENTATION STracking is available as a python library using "pip install" and the source code is publicly available on GitHub (https://github.com/sylvainprigent/stracking). A graphical interface is available using two napari plugins: napari-stracking and napari-tracks-reader. These napari plugins can be installed via the napari plugins menu or using "pip install". The napari plugin source codes are available on GitHub (https://github.com/sylvainprigent/napari-tracks-reader, https://github.com/sylvainprigent/napari-stracking). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sylvain Prigent
- SERPICO Project Team, Inria Centre Rennes-Bretagne Atlantique, F-35042, Rennes, France.,SERPICO Project Team, UMR144 CNRS Institut Curie, PSL Research University, F-75005, Paris, France
| | - Cesar Augusto Valades-Cruz
- SERPICO Project Team, Inria Centre Rennes-Bretagne Atlantique, F-35042, Rennes, France.,SERPICO Project Team, UMR144 CNRS Institut Curie, PSL Research University, F-75005, Paris, France
| | - Ludovic Leconte
- SERPICO Project Team, Inria Centre Rennes-Bretagne Atlantique, F-35042, Rennes, France.,SERPICO Project Team, UMR144 CNRS Institut Curie, PSL Research University, F-75005, Paris, France
| | - Jean Salamero
- SERPICO Project Team, Inria Centre Rennes-Bretagne Atlantique, F-35042, Rennes, France.,SERPICO Project Team, UMR144 CNRS Institut Curie, PSL Research University, F-75005, Paris, France
| | - Charles Kervrann
- SERPICO Project Team, Inria Centre Rennes-Bretagne Atlantique, F-35042, Rennes, France.,SERPICO Project Team, UMR144 CNRS Institut Curie, PSL Research University, F-75005, Paris, France
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Vallotton P, van Oijen AM, Whitchurch CB, Gelfand V, Yeo L, Tsiavaliaris G, Heinrich S, Dultz E, Weis K, Grünwald D. Diatrack particle tracking software: Review of applications and performance evaluation. Traffic 2017; 18:840-852. [PMID: 28945316 PMCID: PMC5677553 DOI: 10.1111/tra.12530] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Revised: 09/21/2017] [Accepted: 09/21/2017] [Indexed: 12/24/2022]
Abstract
Object tracking is an instrumental tool supporting studies of cellular trafficking. There are three challenges in object tracking: the identification of targets; the precise determination of their position and boundaries; and the assembly of correct trajectories. This last challenge is particularly relevant when dealing with densely populated images with low signal-to-noise ratios-conditions that are often encountered in applications such as organelle tracking, virus particle tracking or single-molecule imaging. We have developed a set of methods that can handle a wide variety of signal complexities. They are compiled into a free software package called Diatrack. Here we review its main features and utility in a range of applications, providing a survey of the dynamic imaging field together with recommendations for effective use. The performance of our framework is shown to compare favorably to a wide selection of custom-developed algorithms, whether in terms of localization precision, processing speed or correctness of tracks.
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Affiliation(s)
| | | | | | - Vladimir Gelfand
- Northwestern University Feinberg School of Medicine, Department of Cell and Molecular Biology, Chicago, IL 60611, USA
| | | | | | | | - Elisa Dultz
- ETH Zürich, Institute of Biochemistry, Zürich, Switzerland
| | - Karsten Weis
- ETH Zürich, Institute of Biochemistry, Zürich, Switzerland
| | - David Grünwald
- University of Massachusetts Medical School, RNA Therapeutics Institute and Department of Biochemistry and Molecular Pharmacology, Worcester MA, USA
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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 DOI: 10.1016/j.cels.2016.08.007] [Citation(s) in RCA: 111] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Abstract] [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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Rezatofighi SH, Gould S, Vo BT, Vo BN, Mele K, Hartley R. Multi-Target Tracking With Time-Varying Clutter Rate and Detection Profile: Application to Time-Lapse Cell Microscopy Sequences. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:1336-1348. [PMID: 25594963 DOI: 10.1109/tmi.2015.2390647] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Quantitative analysis of the dynamics of tiny cellular and sub-cellular structures, known as particles, in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels of noise, high target density, complex motion patterns and intricate interactions. In this paper, we propose a framework for tracking these structures based on the random finite set Bayesian filtering framework. We focus on challenging biological applications where image characteristics such as noise and background intensity change during the acquisition process. Under these conditions, detection methods usually fail to detect all particles and are often followed by missed detections and many spurious measurements with unknown and time-varying rates. To deal with this, we propose a bootstrap filter composed of an estimator and a tracker. The estimator adaptively estimates the required meta parameters for the tracker such as clutter rate and the detection probability of the targets, while the tracker estimates the state of the targets. Our results show that the proposed approach can outperform state-of-the-art particle trackers on both synthetic and real data in this regime.
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Godinez WJ, Rohr K. Tracking multiple particles in fluorescence time-lapse microscopy images via probabilistic data association. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:415-432. [PMID: 25252280 DOI: 10.1109/tmi.2014.2359541] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Tracking subcellular structures as well as viral structures displayed as 'particles' in fluorescence microscopy images yields quantitative information on the underlying dynamical processes. We have developed an approach for tracking multiple fluorescent particles based on probabilistic data association. The approach combines a localization scheme that uses a bottom-up strategy based on the spot-enhancing filter as well as a top-down strategy based on an ellipsoidal sampling scheme that uses the Gaussian probability distributions computed by a Kalman filter. The localization scheme yields multiple measurements that are incorporated into the Kalman filter via a combined innovation, where the association probabilities are interpreted as weights calculated using an image likelihood. To track objects in close proximity, we compute the support of each image position relative to the neighboring objects of a tracked object and use this support to recalculate the weights. To cope with multiple motion models, we integrated the interacting multiple model algorithm. The approach has been successfully applied to synthetic 2-D and 3-D images as well as to real 2-D and 3-D microscopy images, and the performance has been quantified. In addition, the approach was successfully applied to the 2-D and 3-D image data of the recent Particle Tracking Challenge at the IEEE International Symposium on Biomedical Imaging (ISBI) 2012.
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