Advancements in Cell Tracking Technology
With today's advanced microscopes, scientists can capture videos of entire embryos developing in real time. However, converting these stunning images into accurate trajectories of each cell's journey is a complex challenge. Cells move, divide, and sometimes disappear as they form tissues and organs, making the process of tracking them extremely difficult.
Scientists often use the nuclei of cells as landmarks to identify boundaries within each video frame—a process known as segmentation. They then track these cells from one frame to the next. Accurate cell tracking is not only crucial for understanding developmental processes but also for studying how diseases develop and how diseased cells respond to treatments.
In Nature Methods, scientists at the Chan Zuckerberg Biohub San Francisco have introduced Ultrack, a cell-tracking platform that can handle everything from a few cells in a lab dish to whole embryos in 3D videos. Ultrack has shown superior performance in whole-embryo cell tracking compared to other tools in the Cell Tracking Challenge, an international benchmarking initiative.
"It's easy to do tracking in 2D or on a few cells, but Ultrack pushes the limits on very hard scenarios, like 3D or full embryos," says Loïc Royer, director of imaging AI at the San Francisco Biohub and senior author of the paper. "It's very fast and scales well but also has a lot of practical features to make it easy to use."
Working Smarter, Not Harder
Traditional cell tracking algorithms follow two steps: first, segmenting the cells in each frame of the video, and then linking the same cells across frames. The main issue with this approach is the initial step, where defining all the cells in a large, blurry, 3D microscopy image is challenging. It's difficult to determine whether a single large cell in a frame is actually multiple cells passing by or two cells that recently divided.
Ultrack takes a different approach by solving both tasks—segmentation and linking—simultaneously. Each time Ultrack inspects a candidate region in a frame, the algorithm creates something called an ultrametric contour map—a hierarchy of boundaries, with possible cell outlines represented as a composition of coarser to finer partitions.
To decide which cell boundary is correct, Ultrack considers all frames, identifying the most consistent cell boundaries over time when connecting to neighboring frames. This method is similar to how the brain determines whether a cloud in the sky is one large structure or two smaller clouds passing each other.
Ultrack simplifies this further by considering only segmentation scenarios that align with the rules of cell biology, such as the fact that cells divide but generally don't merge or make sudden jumps from one place to another. This efficient approach reduces computation time and minimizes tracking errors, allowing researchers to spend less time manually correcting mistakes.
"In images of dense tissue, where every correction requires considerable manual labor, Ultrack roughly halves the time scientists spend fixing segmentation and tracking mistakes," says Biohub SF scientist Jordão Bragantini, first author of the paper. "It does all this without the need for retraining deep-learning models on each new dataset, which is a major hurdle for many labs."
From Zebrafish to Sea Squirts
To evaluate Ultrack's performance in tracking organ development, the team used the zebrafish neuromast—a mechanosensory organ that helps fish navigate—as a model system. Using guidelines from the Cell Tracking Challenge, Ultrack achieved near-perfect accuracy.
The Royer team also used Ultrack to reconstruct the entire developmental trajectories of multiple embryos for Zebrahub, a zebrafish cell atlas published in Cell. Other Biohub scientists are using Ultrack to study the zebrafish immune system.
To explore the massive cell-tracking datasets from Ultrack, Royer's team, in collaboration with colleagues at the Chan Zuckerberg Initiative led by Chi-Li Chiu, developed an innovative tool called inTRACKtive, also published in Nature Methods. At the Biohub, this work was led by Teun Huijben.
Using inTRACKtive's intuitive browser-based interface, users can rotate embryos in 3D space, select groups of cells, and follow their trajectories for deeper analysis. They can speed up or slow down developmental processes and even see events run backward.
Royer's team also uploaded datasets from five other model species—including mouse, C. elegans, and sea squirt—to create the Virtual Embryo Zoo, where users can explore the datasets using inTRACKtive. Since inTRACKtive runs in any browser, users can interact with datasets from laptops, desktops, and even phones.
"We encourage researchers to contribute to the Virtual Embryo Zoo by submitting their own whole-embryo datasets from other species. Contributions will help the resource grow, creating a comprehensive repository of embryonic development across different organisms," says Huijben.
"Next, we plan to expand inTRACKtive's capabilities by integrating imaging data alongside cell tracking results. This would allow for even richer visualizations by layering cell behavior and tissue development with live microscopy."
