The comprehensive reconstruction of cell lineages in complex multicellular organisms is a central goal of developmental biology. We present an open-source computational framework for the segmentation and tracking of cell nuclei with high accuracy and speed. We demonstrate its (i) generality by reconstructing cell lineages in four-dimensional, terabytesized image data sets of fruit fly, zebrafish and mouse embryos acquired with three types of fluorescence microscopes, (ii) scalability by analyzing advanced stages of development with up to 20,000 cells per time point at 26,000 cells per minute on a single computer workstation and (iii) ease of use by adjusting only two parameters across all data sets and providing visualization and editing tools for efficient data curation. Our approach achieves on average 97.0% linkage accuracy across all species and imaging modalities. Using our system, we performed the first cell lineage reconstruction of early Drosophila melanogaster nervous system development, revealing neuroblast dynamics throughout an entire embryo.
The publication of the cell lineaging framework is available from the literature section (Amat, Lemon, Mossing, McDole, Wan, Branson, Myers and Keller 2014, Nature Methods).
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- Download stable release of the automated cell lineaging framework
- Download stable release of the modified CATMAID module for visualizing/editing lineage reconstructions
- Link to latest updates of the automated cell lineaging framework (code, documentation, mailing list)
- Link to latest updates of the modified CATMAID module
- Download collection of Matlab scripts for importing XML tracking data into Matlab
- Download TGMM software user guide