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. Main textIn biosciences, object tracking is an essential image analysis technique used to quantify dynamic processes. In Life Sciences, tracking is used, for instance, to follow single particles, subcellular organelles, bacteria, cells, and whole animals. While tech developments have drastically improved image acquisition capabilities and allowed increasingly sophisticated experimental setups, they have also led to bottlenecks in downstream image analyses. Due to the sheer diversity of images, no single software can address every Life Science research tracking challenge. This has prompted the development of flexible and extensible software tracking platforms 1-5 , including TrackMate, that enable biologists to build automated tracking pipelines tailored to a specific problem.

TrackMate is an automated tracking software used to analyze bioimages and distributed as a Fiji plugin. Here we introduce a new version of TrackMate rewritten to improve performance and usability, and integrating several popular machine and deep learning algorithms to improve versatility. We illustrate how these new components can be used to efficiently track objects from brightfield and fluorescence microscopy images across a wide range of bio-imaging experiments.

The maintenance of neural stem cells (NSCs) in the adult brain depends on their activation frequency and division mode. We use long-term intravital imaging of NSCs in the zebrafish adult telencephalon to link activation and division mode with predictive cellular and molecular parameters. We reveal that apical surface area and expression of the Notch ligand DeltaA predict NSC activation frequency, while deltaA expression marks NSC commitment to neurogenesis. We also find thatdeltaA-negative NSCs constitute the bona fide self-renewing NSC pool and systematically engage in asymmetric divisions generating a self-renewingdeltaAnegand a neurogenicdeltaAposNSC. Finally, modulation of Notch signaling during imaging indicates that the prediction of activation frequency by apical size, and the asymmetric divisions ofdeltaAnegNSCs, are functionally independent of Notch. These results provide dynamic qualitative and quantitative readouts of NSC lineage progression in vivo and support a hierarchical organization of NSCs in differently fated sub-populations.