TrackMate v7, cell tracking for everyone with (almost) everything
We proudly present a new version of TrackMate, which provides a collection of new detectors, including deep-learning models, and new visualisation and analysis tools that strongly improve and facilitate studies on the dynamics of cells and objects.
TrackMate is your buddy for your everyday tracking
TrackMate (Tinevez et al., 2017) is an open-source Fiji plugin initially made for tracking single particles imaged with fluorescence microscopes. Created by Jean-Yves Tinevez from Institut Pasteur, France, TrackMate is one of the most popular tracking tools for bioimage analysis.
Despite its popularity, TrackMate had some limitations in object detection and analysis. For example, before the release of TrackMate v7, TrackMate could only detect and track blob-like structures. It was problematic to detect non-blob-like structures (such as migrating cells) and measure their morphology or signal over time. Jean-Yves teamed up with Guillaume Jacquemet from Åbo Akademi University, Finland, to tackle these limitations. Together, they gathered a young scientist's dream team, including Dmitry Ershov, Minh-Son Phan and Stéphane U. Rigaud from the Institut Pasteur and myself from Åbo Akademi University and Turku BioImaging, to work in developing and testing a new version of TrackMate. This project has been a wonderful example of how people from different fields come together and create something genuinely awesome (Ershov, Phan, Pylvänäinen, Rigaud et al., 2022). Here are some of the highlights of our work.
TrackMate v7 has improved object detection
The previous versions of the TrackMate had excellent detectors for light blob-like objects, such as round fluorescent cells. Still, they struggled when trying to detect irregular objects or objects imaged in transmitted-light microscopy. As image segmentation, machine-learning (ML), and deep learning (DL) methods have taken a massive leap forward; it was time to integrate their power into the new TrackMate detectors. The new TrackMate detectors (figure 1) allow the detection of previously segmented objects from label images, binary mask images and segmentation probability maps. As a cherry on top of the cake, Jean-Yves incorporated DL and ML segmentation algorithms, such as StarDist (Schmidt et al., 2018), to the TrackMate, making it possible to apply custom-made segmentation algorithms to a specific dataset directly. These segmentation improvements elevated TrackMate as a tracking tool to an entirely new level.
Figure 1: New TrackMate detectors. Popular segmentation tools, such as ilastik, the Weka Trainable-Segmentation Fiji plugin, StarDist, cellpose (Stringer et al., 2020) and the morphological segmentation tool MorphoLibJ have now been incorporated into the new TrackMate. The New TrackMate can now also track previously segmented objects by directly importing mask or label images.
Here is an example of one of the new detectors in action:
Video 1: Migration of MCF10DCIS.com cells, labelled with SiR-DNA, recorded using a spinning disk confocal microscope and automatically tracked using the threshold detector. Each colour corresponds to a unique track ID.
TrackMate v7 has new analysis and visualisation capabilities
Another fantastic development of TrackMate v7 is its improved analysis tools. The previous version of TrackMate was able to measure the objects’ position and tracks. Now, it is possible to extract information about the objects’ morphological features and even signal intensity over time. This allowed us to correlate cells’ motility with changes in their shape and the fluorescent intensity over time. To showcase this feature, I used a pre-trained DL model (StarDist) first to segment nuclei and then to measure the signal oscillation of an ERK-activity reporter in the segmented nuclei and visualised this oscillation as heat maps (Figure 2).
Figure 2: Following ERK activity in migrating cells. MDA-MB-231 cells stably expressing an ERK activity reporter (ERK-KTR-Clover) and labelled using SiR-DNA were recorded live using a wide-field fluorescent microscope for 2 hours (1 image every minute). The “Versatile fluorescent nuclei” StarDist model was used to track the 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 low, yellow high). Heatmaps were generated using PlotTwist. Scale bar = 250 μm.
One of my favourite features of TrackMate v7 is the possibility to create 3D label images. Using this method, objects or cells are tracked through a z-stack, slice by slice, instead of time. Overlapping objects will be considered to belong to the same 3D object and a 3D label is created. This is a handy alternative for some of those currently existing time-consuming and computationally heavy 3D segmentation solutions.
Video 2: MCF10DCIS.com 3D spheroids were stained for Dapi and imaged using a spinning disk confocal microscope. The cell nuclei were detected at each z plane using StarDist and tracked. Tracked nuclei were then exported as a label image to create 3D labels. The video was created using the FPBioimage helper (Fantham & Kaminski, 2017).
As an impatient biologist, I (as do so many others) get often irritated by complicated software installation protocols and poorly written user instructions. For this reason, we wanted to ensure that TrackMate v7 is accessible as a tool to all biologists. It is super easy to install through multiple Fiji update sites. One of my major contributions to the paper was to write multiple step-by-step instructions on how to use different detectors (https://imagej.net/plugins/TrackMate/TrackMate-v7-detectors). In addition, we provided a visual guide for how to select a detector (Figure 3).
Figure 3. Cheatsheet for choosing the best TrackMate detector for your use case.
The new version of TrackMate has already revolutionized how we can study cell or object movements, making the new TrackMate your very best buddy for your everyday tracking. The story of TrackMate does not end here, it will be continuously developed and improved through a great network of tracking enthusiasts. To support this, let’s all make some great science using the new TrackMate. I know I will!
You can find the full paper here: https://www.nature.com/articles/s41592-022-01507-1
Ershov, D., Phan, M.-S., Pylvänäinen, J. W., Rigaud, S. U., Le Blanc, L., Charles-Orszag, A., Conway, J. R. W., Laine, R. F., Roy, N. H., Bonazzi, D., Duménil, G., Jacquemet, G., & Tinevez, J.-Y. (2022). TrackMate 7: Integrating state-of-the-art segmentation algorithms into tracking pipelines. Nature Methods, 1–4. https://doi.org/10.1038/s41592-022-01507-1
Tinevez, J.-Y., Perry, N., Schindelin, J., Hoopes, G. M., Reynolds, G. D., Laplantine, E., Bednarek, S. Y., Shorte, S. L., & Eliceiri, K. W. (2017). TrackMate: An open and extensible platform for single-particle tracking. Methods (San Diego, Calif.), 115, 80–90. https://doi.org/10.1016/j.ymeth.2016.09.016
Schmidt, U., Weigert, M., Broaddus, C., & Myers, G. (2018). Cell Detection with Star-Convex Polygons. In A. F. Frangi, J. A. Schnabel, C. Davatzikos, C. Alberola-López, & G. Fichtinger (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (pp. 265–273). Springer International Publishing. https://doi.org/10.1007/978-3-030-00934-2_30