Automated analysis of the mitotic phases of human cells in 3D fluorescence microscopy image sequences.
The evaluation of fluorescence microscopy images acquired in high-throughput cell phenotype screens constitutes a substantial bottleneck and motivates the development of automated image analysis methods. Here we introduce a computational scheme to process 3D multi-cell time-lapse images as they are produced in large-scale RNAi experiments. We describe an approach to automatically segment, track, and classify cell nuclei into different mitotic phases. This enables automated analysis of the duration of single phases of the cell life cycle and thus the identification of cell cultures that show an abnormal mitotic behavior. Our scheme proves a high accuracy, suggesting a promising future for automating the evaluation of high-throughput experiments.
Harder
Nathalie
N
University of Heidelberg, IPMB, and DKFZ Heidelberg, Dept. Bioinformatics and Functional Genomics, Im Neuenheimer Feld 364, D-69120 Heidelberg, Germany. n.harder@dkfz-heidelberg.de
Mora-Bermúdez
Felipe
F
Godinez
William J
WJ
Ellenberg
Jan
J
Eils
Roland
R
Rohr
Karl
K
D000465:Algorithms; D001185:Artificial Intelligence; D002467:Cell Nucleus / Q000648:ultrastructure*; D006367:HeLa Cells; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D018715:Microscopy, Video / Q000379:methods*; D008938:Mitosis; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique