Detection and tracking of overlapping cell nuclei for large scale mitosis analyses

BMC Bioinformatics

Methodology Article

Florencia di Pietro¹  Xavier Morin¹  France Rose²  Auguste Genovesio²  Yingbo Li³ 

[1] Division cellulaire et neurogenèse, Institut de Biologie de l’Ecole Normale Superieure, PSL Research University, 46, rue d’Ulm, 75005, Paris, France;Scientific Center for Computational Biology, Institut de Biologie de l’Ecole Normale Superieure, CNRS-INSERM-ENS, PSL Research University, 46, rue d’Ulm, 75005, Paris, France;Scientific Center for Computational Biology, Institut de Biologie de l’Ecole Normale Superieure, CNRS-INSERM-ENS, PSL Research University, 46, rue d’Ulm, 75005, Paris, France;Division cellulaire et neurogenèse, Institut de Biologie de l’Ecole Normale Superieure, PSL Research University, 46, rue d’Ulm, 75005, Paris, France;

关键词: Image analysis;    Gaussian mixture;    High throughput;    Mitosis;    Time-lapse microscopy;    Cell detection;   

DOI  :  10.1186/s12859-016-1030-9

 received in 2016-03-10, accepted in 2016-04-09,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundCell culture on printed micropatterns slides combined with automated fluorescent microscopy allows for extraction of tens of thousands of videos of small isolated growing cell clusters. The analysis of such large dataset in space and time is of great interest to the community in order to identify factors involved in cell growth, cell division or tissue formation by testing multiples conditions. However, cells growing on a micropattern tend to be tightly packed and to overlap with each other. Consequently, image analysis of those large dynamic datasets with no possible human intervention has proven impossible using state of the art automated cell detection methods.ResultsHere, we propose a fully automated image analysis approach to estimate the number, the location and the shape of each cell nucleus, in clusters at high throughput. The method is based on a robust fit of Gaussian mixture models with two and three components on each frame followed by an analysis over time of the fitting residual and two other relevant features. We use it to identify with high precision the very first frame containing three cells. This allows in our case to measure a cell division angle on each video and to construct division angle distributions for each tested condition. We demonstrate the accuracy of our method by validating it against manual annotation on about 4000 videos of cell clusters.ConclusionsThe proposed approach enables the high throughput analysis of video sequences of isolated cell clusters obtained using micropatterns. It relies only on two parameters that can be set robustly as they reduce to the average cell size and intensity.

【 授权许可】

CC BY   
© Li et al. 2016

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