Analysis of in vivo single cell behavior by high throughput, human-in-the-loop segmentation of three-dimensional images
Analysis of single cells in their native environment is a powerful method to
address key questions in developmental systems biology. Confocal microscopy
imaging of intact tissues, followed by automatic image segmentation, provides a
means to conduct cytometric studies while at the same time preserving crucial
information about the spatial organization of the tissue and morphological
features of the cells. This technique is rapidly evolving but is still not in
widespread use among research groups that do not specialize in technique
development, perhaps in part for lack of tools that automate repetitive tasks
while allowing experts to make the best use of their time in injecting their
domain-specific knowledge.
Here we focus on a well-established stem cell model system, the C. elegans
gonad, as well as on two other model systems widely used to study cell fate
specification and morphogenesis: the pre-implantation mouse embryo and the
developing mouse olfactory epithelium. We report a pipeline that integrates
machine-learning-based cell detection, fast human-in-the-loop curation of these
detections, and running of active contours seeded from detections to segment
cells. The procedure can be bootstrapped by a small number of manual
detections, and outperforms alternative pieces of software we benchmarked on C.
elegans gonad datasets. Using cell segmentations to quantify fluorescence
contents, we report previously-uncharacterized cell behaviors in the model
systems we used. We further show how cell morphological features can be used to
identify cell cycle phase; this provides a basis for future tools that will
streamline cell cycle experiments by minimizing the need for exogenous cell
cycle phase labels.
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Text Reference
Michael Chiang, Sam Hallman, Amanda Cinquin, Nabora Reyes de Mochel, Adrian Paz, Shimako Kawauchi, Anne Calof, Ken Cho, Charless Fowlkes, and Oliver Cinquin. Analysis of in vivo single cell behavior by high throughput, human-in-the-loop segmentation of three-dimensional images. BMC Bioinformatics, 2015.BibTeX Reference
@article{ChiangHCRPKCCFC_BMCB_2015,AUTHOR = "Chiang, Michael and Hallman, Sam and Cinquin, Amanda and de Mochel, Nabora Reyes and Paz, Adrian and Kawauchi, Shimako and Calof, Anne and Cho, Ken and Fowlkes, Charless and Cinquin, Oliver",
TITLE = "Analysis of in vivo single cell behavior by high throughput, human-in-the-loop segmentation of three-dimensional images",
JOURNAL = "BMC Bioinformatics",
YEAR = "2015",
VOLUME = "16:397",
TAG = "biological_images"
}