Tracking cells in three-dimensional cell models such as organoids often requires lengthy manual review work. However, biophysicists at the AMOLF physics research institute have now developed a new algorithm published in Nature Methods that can track cells more efficiently and automatically identify any errors.

3D cell tracking software enables researchers to track the development of cells in tissues automatically. Using neural networks, these programmes recognise where cells are moving and where new cells have originated in a series of microscope images. However, this method can be inaccurate in tissues with large numbers of cells, which requires time-consuming manual correction.

‘In our group, we are investigating the development of intestinal organoids, which are pieces of intestinal tissue that we grow in the laboratory’, says Max Betjes, a PhD student and biophysicist at the AMOLF physics research institute. ‘Tracking is very difficult in this case because the intestinal cells are very close together and divide very quickly. The manual correction work involved can easily account for half of a PhD programme.’ This is why Betjes developed a new algorithm that not only tracks cells more effectively, but also enables the underlying neural network to highlight potential errors. This makes it much easier to identify which parts of the cell tracking need correcting. Betjes explains, ‘In this way, our algorithm increases the throughput of experiments with organoids.’