A quantitative view of cell lineages

Lineage tracing has long been the holy grail for biologists aiming to unravel how a single cell develops into a complex organism. While John Sulston’s pioneered manual reconstruction of C. elegans lineages and showcased the potential of such complex dataset, modern acquisition systems, such as fluorescence light-sheet imaging, now generate terabyte-scale datasets for intricate organisms like zebrafish, mice, and Drosophila.

Recent years have seen major advances in algorithms for automatically or semi-automatically reconstructing cell lineages, producing highly complex spatio-temporal datasets. Yet, what has remained elusive are effective tools for manipulating, querying, and characterizing these datasets.

In this presentation, I will introduce lineagetree and napari-ReLAX (Reconstructed Lineages Analysis and eXploration), two open-source Python libraries designed for seamless, hassle-free exploration of any tracking dataset. I will then introduce the unordered constrained tree edit distance which allows to measure in an agnostic manner any two cell lineage trees (or tree-like structures). Finally I will showcase the usefulness of this metric across multiple example in C. elegans, P. hawaiensis and other systems.