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, terabyte-sized 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 min(-1) 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.
Optimal image quality in light-sheet microscopy requires a perfect overlap between the illuminating light sheet and the focal plane of the detection objective. However, mismatches between the light-sheet and detection planes are common owing to the spatiotemporally varying optical properties of living specimens. Here we present the AutoPilot framework, an automated method for spatiotemporally adaptive imaging that integrates (i) a multi-view light-sheet microscope capable of digitally translating and rotating light-sheet and detection planes in three dimensions and (ii) a computational method that continuously optimizes spatial resolution across the specimen volume in real time. We demonstrate long-term adaptive imaging of entire developing zebrafish (Danio rerio) and Drosophila melanogaster embryos and perform adaptive whole-brain functional imaging in larval zebrafish. Our method improves spatial resolution and signal strength two to five-fold, recovers cellular and sub-cellular structures in many regions that are not resolved by non-adaptive imaging, adapts to spatiotemporal dynamics of genetically encoded fluorescent markers and robustly optimizes imaging performance during large-scale morphogenetic changes in living organisms.
We present the Real-time Accurate Cell-shape Extractor (RACE), a high-throughput image analysis framework for automated three-dimensional cell segmentation in large-scale images. RACE is 55-330 times faster and 2-5 times more accurate than state-of-the-art methods. We demonstrate the generality of RACE by extracting cell-shape information from entire Drosophila, zebrafish, and mouse embryos imaged with confocal and light-sheet microscopes. Using RACE, we automatically reconstructed cellular-resolution tissue anisotropy maps across developing Drosophila embryos and quantified differences in cell-shape dynamics in wild-type and mutant embryos. We furthermore integrated RACE with our framework for automated cell lineaging and performed joint segmentation and cell tracking in entire Drosophila embryos. RACE processed these terabyte-sized datasets on a single computer within 1.4 days. RACE is easy to use, as it requires adjustment of only three parameters, takes full advantage of state-of-the-art multi-core processors and graphics cards, and is available as open-source software for Windows, Linux, and Mac OS.
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