Analysis of in vivo single cell behavior by high throughput, human-in-the-loop segmentation of three-dimensional images

BMC Bioinformatics

Methodology Article

Shimako Kawauchi¹  Sam Hallman²  Charless C. Fowlkes²  Adrian Paz³  Michael Chiang³  Amanda Cinquin³  Nabora Reyes de Mochel³  Olivier Cinquin³  Ken W. Cho³  Anne L. Calof⁴ 

[1] Center for Complex Biological Systems, University of California at Irvine, Irvine, USA;Center for Complex Biological Systems, University of California at Irvine, Irvine, USA;Department of Computer Science, University of California at Irvine, Irvine, USA;Department of Developmental & Cell Biology, University of California at Irvine, Irvine, USA;Center for Complex Biological Systems, University of California at Irvine, Irvine, USA;Department of Developmental & Cell Biology, University of California at Irvine, Irvine, USA;Center for Complex Biological Systems, University of California at Irvine, Irvine, USA;Department of Anatomy & Neurobiology, University of California at Irvine, Irvine, USA;

关键词: Spatial cytometry;    3D image segmentation;    Stem cells;    Cell cycle;    C. elegans;    Mouse pre-implantation embryo;    Olfactory placode;    Olfactory epithelium;   

DOI  :  10.1186/s12859-015-0814-7

 received in 2015-02-15, accepted in 2015-10-31,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundAnalysis 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.ResultsHere 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.ConclusionsHigh-throughput 3D segmentation makes it possible to extract rich information from images that are routinely acquired by biologists, and provides insights — in particular with respect to the cell cycle — that would be difficult to derive otherwise.

【 授权许可】

CC BY   
© Chiang et al. 2015