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TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines

Abstract

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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Fig. 1: The new capabilities of TrackMate.
Fig. 2: TrackMate can be used to track objects from a wide variety of bio-imaging experiments.

Data Availability

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.

Code Availability

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.

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Acknowledgements

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.

Author information

Authors and Affiliations

Authors

Contributions

G. J. and J.-Y. T. conceived the project; J.-Y. T. wrote the source code; G. J., J. W. P., N. H. R., and L. L. B. performed the image acquisition of the test and example data; G. J., J. W. P., R. F. L., J.-Y. T., M.-S. P., D. E., and S. U. R. tested the code; J. R. W. C., D. B., G. D. and A. C.-O. provided critical reagents; G. J., J. W. P., J.-Y. T., M.-S. P., D. E., S. U. R., and J.-Y. T. wrote the documentation and tutorials.; G. J. and J.-Y. T. wrote the manuscript with input from all co-authors.

Corresponding authors

Correspondence to Guillaume Jacquemet or Jean-Yves Tinevez.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks the anonymous reviewers for their contribution to the peer review of this work. Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Following ERK activity in migrating cells.

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.

Extended Data Fig. 2 Tracking focal adhesions in endothelial cells using Weka and TrackMate.

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.

Extended Data Fig. 3 Tracking label images using TrackMate.

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.

Extended Data Fig. 4 Tracking 2D labels to generate 3D labels using TrackMate.

(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.

Extended Data Fig. 5 The TrackMate Helper module.

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 information

Supplementary Information

Supplementary Note 1 and TrackMate v7 documentation and tutorials.

Reporting Summary

Supplementary Video 1

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.

Supplementary Video 2

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.

Supplementary Video 3

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.

Supplementary Video 4

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.

Supplementary Video 5

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.

Supplementary Video 6

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.

Supplementary Video 7

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.

Supplementary Video 8

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.

Supplementary Video 9

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.

Supplementary Video 10

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.

Supplementary Video 11

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.

Supplementary Tables 1 and 2

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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

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