TrackMate is an automated tracking software used to analyze bioimages and is distributed as a Fiji plugin. Here, we introduce a new version of TrackMate. TrackMate 7 is built to address the broad spectrum of modern challenges researchers face by integrating state-of-the-art segmentation algorithms into tracking pipelines. We illustrate qualitatively and quantitatively that these new capabilities function effectively across a wide range of bio-imaging experiments.
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Communications Biology Open Access 09 July 2022
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All of the new data used in this article are available on Zenodo, under a dedicated collection (https://zenodo.org/communities/trackmate). They are publicly available under the Creative Commons Attribution 4.0 International license.
TrackMate 7 and TrackMate-Helper introduced and used in this article are open-source software (GNU General Public License v3.0). Their source code is available on GitHub (https://github.com/fiji/TrackMate and https://github.com/tinevez/TrackMate-CTCRunner). TrackMate 7 is directly available in the Fiji software by simply updating it. TrackMate is documented on the ImageJ wiki: https://imagej.net/plugins/trackmate/ and the documentation for the new features can be accessed from https://imagej.net/plugins/trackmate/trackmate-v7-detectors.
Sbalzarini, I. F. & Koumoutsakos, P. Feature point tracking and trajectory analysis for video imaging in cell biology. J. Struct. Biol. 151, 182–195 (2005).
Chenouard, N., Bloch, I. & Olivo-Marin, J.-C. Multiple hypothesis tracking for cluttered biological image sequences. IEEE Trans. Pattern Anal. Mach. Intell. 35, 2736–3750 (2013).
Piccinini, F., Kiss, A. & Horvath, P. CellTracker (not only) for dummies. Bioinformatics 32, 955–957 (2016).
Tinevez, J.-Y. et al. TrackMate: an open and extensible platform for single-particle tracking. Methods 115, 80–90 (2017).
McQuin, C. et al. CellProfiler 3.0: Next-generation image processing for biology. PLoS Biol. 16, e2005970 (2018).
Chenouard, N. et al. Objective comparison of particle tracking methods. Nat. Methods 11, 281–289 (2014).
Moen, E. et al. Deep learning for cellular image analysis. Nat. Methods 16, 1233–1246 (2019).
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
Sage, D., Neumann, F. R., Hediger, F., Gasser, S. M. & Unser, M. Automatic tracking of individual fluorescence particles: application to the study of chromosome dynamics. IEEE Trans. Image Process. 14, 1372–1383 (2005).
Berg, S. et al. ilastik: interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232 (2019).
Arganda-Carreras, I. et al. Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics 33, 2424–2426 (2017).
Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021).
Legland, D., Arganda-Carreras, I. & Andrey, P. MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ. Bioinformatics 32, 3532–3534 (2016).
Schmidt, U., Weigert, M., Broaddus, C. & Myers, G. Cell detection with star-convex polygons. in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (eds. Frangi, A. F., Schnabel, J. A., Davatzikos, C., Alberola-López, C. & Fichtinger, G.) 265–273 (Springer International Publishing, 2018).
von Chamier, L. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat. Commun. 12, 2276 (2021).
Caicedo, J. C. et al. Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl. Nat. Methods 16, 1247–1253 (2019).
Lutolf, M. P., Doyonnas, R., Havenstrite, K., Koleckar, K. & Blau, H. M. Perturbation of single hematopoietic stem cell fates in artificial niches. Integr. Biol. Quant. Biosci. Nano Macro 1, 59–69 (2009).
Haase, R. et al. CLIJ: GPU-accelerated image processing for everyone. Nat. Methods 17, 5–6 (2020).
Haase, R. clij/TrackMate-clij2: 220.127.116.11-doi. (Zenodo, 2022); https://doi.org/10.5281/zenodo.5983244
Regot, S., Hughey, J. J., Bajar, B. T., Carrasco, S. & Covert, M. W. High-sensitivity measurements of multiple kinase activities in live single cells. Cell 157, 1724–1734 (2014).
Kudo, T. et al. Live-cell measurements of kinase activity in single cells using translocation reporters. Nat. Protoc. 13, 155–169 (2018).
Jacquemet, G. Combining StarDist and TrackMate Example 1—Breast Cancer Cell Dataset (2020); https://doi.org/10.5281/zenodo.4034976
Jacquemet, G. et al. FiloQuant reveals increased filopodia density during breast cancer progression. J. Cell Biol. 216, 3387–3403 (2017).
Jacquemet, G., Pylvänäinen, J. W. & Tinevez, J.-Y. Tracking Breast Cancer Cells Migrating Collectively and Imaged in Fluorescence with TrackMate-Cellpose (2022); https://doi.org/10.5281/zenodo.5864646
Tinevez, J.-Y., Jacquemet, G. & Pylvänäinen, J. W. Tracking Label Images with TrackMate (2021); https://doi.org/10.5281/zenodo.5221424
Fazeli, E. et al. Automated cell tracking using StarDist and TrackMate. F1000Res. 9, 1279 (2020).
Tinevez, J.-Y., Jacquemet, G. & Roy, N. H. T cells Migration Followed with TrackMate (2021); https://doi.org/10.5281/zenodo.5206119
Roy, N. H. & Jacquemet, G. Combining StarDist and TrackMate Example 2—T Cell Dataset (2020); https://doi.org/10.5281/zenodo.4034929
Bloice, M. D., Roth, P. M. & Holzinger, A. Biomedical image augmentation using Augmentor. Bioinformatics 35, 4522–4524 (2019).
Goedhart, J. PlotTwist: A web app for plotting and annotating continuous data. PLoS Biol. 18, e3000581 (2020).
Tinevez, J.-Y. & Pylvänäinen, J. W. Cell Migration with ERK Signalling (2021); https://doi.org/10.5281/zenodo.5205955
Jacquemet, G., Pylvänäinen, J. W. & Tinevez, J.-Y. Tracking Glioblastoma–Astrocytoma Cells Imaged in Brightfield with TrackMate-Cellpose (2022); https://doi.org/10.5281/zenodo.5863317
Nassif, X. et al. Antigenic variation of pilin regulates adhesion of Neisseria meningitidis to human epithelial cells. 8, 719–725 (1993).
Ke, S.-H. & Madison, E. L. Rapid and efficient site-directed mutagenesis by single-tube ‘megaprimer’ PCR method. Nucleic Acids Res. 25, 3371–3372 (1997).
Soyer, M. et al. Early sequence of events triggered by the interaction of Neisseria meningitidis with endothelial cells. Cell. Microbiol. 16, 878–895 (2014).
Morales, V. M., Bäckman, A. & Bagdasarian, M. A series of wide-host-range low-copy-number vectors that allow direct screening for recombinants. Gene 97, 39–47 (1991).
Geoffroy, M.-C., Floquet, S., Métais, A., Nassif, X. & Pelicic, V. Large-scale analysis of the meningococcus genome by gene disruption: resistance to complement-mediated lysis. Genome Res. 13, 391–398 (2003).
Georgiadou, M., Castagnini, M., Karimova, G., Ladant, D. & Pelicic, V. Large-scale study of the interactions between proteins involved in type IV pilus biology in Neisseria meningitidis: characterization of a subcomplex involved in pilus assembly. Mol. Microbiol. 84, 857–873 (2012).
Le Blanc, L., Rigaud, S. & Tinevez, J.-Y. Neisseria meningitidis Bacterial Growth (2021); https://doi.org/10.5281/zenodo.5419619
Hakanpaa, L. et al. Targeting β1-integrin inhibits vascular leakage in endotoxemia. Proc. Natl Acad. Sci. USA 115, E6467–E6476 (2018).
Jacquemet, G., Minh-Son-Phan & Tinevez, J.-Y. Tracking Focal Adhesions with TrackMate and Weka—Tutorial Dataset 2 (2022); https://doi.org/10.5281/zenodo.5978940
Fantham, M. & Kaminski, C. F. A new online tool for visualization of volumetric data. Nat. Photonics 11, 69–69 (2017).
Tinevez, J.-Y., Pylvänäinen, J. W. & Jacquemet, G. Segmenting Cells in a Spheroid in 3D using 2D StarDist within TrackMate (2021); https://doi.org/10.5281/zenodo.5220610
Kar, A. Original Stacks and Segmented Data (2021); https://doi.org/10.6084/m9.figshare.14447079.v1
Ulman, V. et al. An objective comparison of cell-tracking algorithms. Nat. Methods 14, 1141–1152 (2017).
Maška, M. et al. A benchmark for comparison of cell tracking algorithms. Bioinformatics 30, 1609–1617 (2014).
The integration of existing algorithms as new detectors in TrackMate has been made possible thanks to the high quality of the code, documentation, and support provided by their respective authors. In particular, we would like to thank A. Kreshuk, D. Legland, D. Kutra, I. Arganda-Carreras, C. Stringer, M. Pachitariu, M. Weigert, S. Culley, and U. Schmidt. We can only hope for TrackMate to reach such a standard of quality to become a better tool of science. We are also grateful for the support and help of the bioimage analysis community, in particular C. Rueden, J. Eglinger, N. Chiaruttini, R. Guiet, O. Burri, V. Ulman, T. Pietzsch, and P. Tomancak. We thank H. Blau for giving us the permission to use the ‘mouse hematopoietic stem cells in hydrogel microwells’ dataset made available on the Cell Tracking Challenge website. The authors thank H. Hamidi for her critical reading of the manuscript. This study was supported by France BioImaging (Investissement d’Avenir; ANR-10-INBS-04, J.-Y. T.), the Academy of Finland (338537, G. J.), the Sigrid Juselius Foundation (G. J.), the Cancer Society of Finland (G. J.), the Åbo Akademi University Research Foundation (G. J., CoE CellMech), the Drug Discovery and Diagnostics strategic funding to Åbo Akademi University (G. J.) and the European Union’s Horizon 2020 research and innovation program under Marie Sklodowska-Curie grant agreement 841973 (J. R. W. C.). J. W. P. was supported by Health Campus Turku 2.0 funded by the Academy of Finland. R. F. L. was supported by an MRC Skills development fellowship (MR/T027924/1). The Cell Imaging and Cytometry Core facility (Turku Bioscience, University of Turku, Åbo Akademi University, and Biocenter Finland) and Turku Bioimaging are acknowledged for services, instrumentation, and expertise.
The authors declare no competing interests.
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U2OS (a, b.) and MDA-MB-231 cells (c. and d.) stably expressing an ERK activity reporter (ERK-KTR-Clover) and labeled using SiR-DNA were recorded live using a widefield fluorescence microscope. U2OS cells were recorded live over 3 hours (1 image every 5 minutes) and MDA-MB-231 cells were recorded live over 2 hours (1 image every minute). Cell nuclei were automatically tracked over time by using StarDist in TrackMate. A custom StarDist model was trained to detect the U2OS nuclei using the ZeroCostDL4Mic platform. The “Versatile fluorescent nuclei” StarDist model was used to track the MDA-MB-231 cell nuclei. For each tracked cell, the average intensity of the ERK reporter was measured in their nucleus over time (directly in TrackMate). Changes in ERK activity are displayed as heatmaps (blue high, yellow low). Heatmaps were generated using PlotTwist. Scale bar = 250 µm.
Endothelial cells expressing paxillin-GFP were recorded live using a spinning disk confocal microscope. Focal adhesions were then segmented and tracked using Weka integrated within TrackMate (Movie 4). Raw data (inverted LUT), Weka segmentation results, tracked focal adhesion, and the focal adhesion tracks are displayed for selected time points. Tracked focal adhesions are color-coded to indicate their lifetime (red, long-lived, blue short-lived). In the bottom panel, track colors indicate ID. Scale bar = 25 µm.
a. Mouse hematopoietic stem cells migrating in a hydrogel microwell were automatically segmented using cellpose (Cyto model) implemented in the ZeroCostDL4Mic platform. The resulting label images were automatically tracked using TrackMate (Movie 7). Example raw and label images, as well as local and full cell tracks, are displayed. Yellow squares highlight regions of interest that are magnified. Scale bar = 250 µm. This dataset is available from the Cell Tracking Challenge. b. MCF10DCIS.com cells stably expressing lifeact-RFP and labeled with SiR-DNA were recorded live using a spinning disk confocal microscope. Cells were segmented using cellpose (Cyto model) implemented in the ZeroCostDL4Mic platform. The resulting label images were tracked using TrackMate (Movie 8). Example raw and label images, as well as local and full cell tracks, are displayed. Yellow squares highlight regions of interest that are magnified. Scale bar = 250 µm.
(a.) Confocal images of Arabidopsis thaliana floral meristem and (b.) light-sheet images of a developing Drosophila melanogaster embryo were automatically segmented using cellpose 2D (Cyto2 model) implemented in the ZeroCostDL4Mic platform. Representative single Z plane and the corresponding cellpose segmentation results are displayed. To generate 3D labels, cellpose 2D segmentation results were then tracked using TrackMate. 3D rendering of the raw data and the 3D segmentation results are also shown. Scale bars: (a) = 25 µm, (b) = 100 µm.
Screenshot highlighting the user interface of TrackMate helper, a module that performs systematic parameter sweeps over any user-defined combination of TrackMate detectors and particle-linking algorithms. Using the ground truth provided by the user, TrackMate helper computes the Cell-Tracking -Challenge metrics to help users choose the best detector/tracker combination for their data.
Supplementary Note 1 and TrackMate v7 documentation and tutorials.
Using StarDist within TrackMate to track migrating cancer cells. MCF10DCIS.com cells, labeled with Sir-DNA, were recorded using a spinning-disk confocal microscope and automatically tracked using StarDist integrated within TrackMate. Detected nuclei and local tracks are displayed. The color indicates ID.
Using StarDist within TrackMate to track migrating T cells. Activated T cells plated on ICAM-1 were recorded using a brightfield microscope and automatically tracked using StarDist integrated within TrackMate. The color indicates mean speed.
Measuring ERK activity in migrating cancer cells. MDA-MB-231 cells expressing ERK-KTR-GFP and labeled with Sir-DNA, were recorded using a widefield microscope and automatically tracked using StarDist integrated within TrackMate. Only tracks remaining in the field of view over the whole duration of the movie are displayed. The color indicates ID.
Using Weka within TrackMate to track focal adhesions. Endothelial cells expressing paxillin-GFP were recorded live using a spinning-disk confocal microscope. A custom Weka pixel classifier trained to segment focal adhesion was then loaded directly into TrackMate to track focal adhesions. In the middle panel, focal adhesions are color-coded to indicate their lifetime (red, long-lived; blue, short-lived). In the right panel, track colors indicate ID.
Using ilastik within TrackMate to follow bacteria growth. The growth of Neisseria meningitidis expressing PilQ-mCherry was recorded using a spinning-disk confocal microscope. An ilastik pixel classifier trained to segment individual bacterium was loaded directly into TrackMate to follow bacteria growth.
Using cellpose and TrackMate to track glioblastoma-astrocytoma cells migrating on a polyacrylamide gel. Glioblastoma-astrocytoma cells migrating on a polyacrylamide gel were automatically segmented using a custom cellpose model. The resulting label images were tracked using TrackMate.
Using cellpose and TrackMate to track stem cells. Mouse hematopoietic stem cells migrating in a hydrogel microwell were automatically segmented using cellpose. The resulting label images were tracked using TrackMate. In the bottom left panel, the color of the object indicates the distance traveled (red, longest distance; blue, shortest distance). In the bottom right panel, track colors indicate ID.
Using cellpose and TrackMate to track migrating cancer cells. MCF10DCIS.com cells expressing lifeact-RFP, labeled with Sir-DNA, were recorded using a spinning-disk confocal microscope. Cells were segmented using cellpose, and label images were tracked using TrackMate. The color indicates ID.
Using StarDist 2D within TrackMate to generate 3D labels. MCF10 DCIS.com spheroids were imaged using a spinning-disk confocal microscope. To generate 3D labels, nuclei were detected and tracked across the Z volume using StarDist implemented in TrackMate. The 3D rendering was performed using the FPBioimage software.
Using cellpose 2D and TrackMate to segment 3D images of Arabidopsis thaliana floral meristem. Confocal images of Arabidopsis thaliana floral meristem were segmented using cellpose 2D implemented in ZeroCostDL4Mic. TrackMate was used to track the 2D labels across the Z volume and generate 3D labels. The FPBioimage software was used to perform the 3D rendering.
Using cellpose 2D and TrackMate to segment 3D images of a developing Drosophila melanogaster embryo. Light-sheet microscopy images of a developing Drosophila melanogaster embryo were segmented using cellpose (2D). TrackMate was then used to track the 2D labels across the Z volume and generate 3D labels. The FPBioimage software was used to perform the 3D rendering.
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Ershov, D., Phan, MS., Pylvänäinen, J.W. et al. TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines. Nat Methods 19, 829–832 (2022). https://doi.org/10.1038/s41592-022-01507-1
Communications Biology (2022)