Christian Dietz M.Sc. University of Konstanz
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1 Quantification of dynamic recruitment of repair factors to DNA-Damage and Automated localization, tracking and classification of dividing cells in live cell movies Christian Dietz M.Sc. University of Konstanz
2 INCIDE Interdisciplinary Center for interactive Data Analysis, Modeling and Visual Exploration Bridge between: Computer Science Chemistry Biology Mathematics... Main topics: Analysis and visualization of large amounts of data High-throughput image processing
3 INCIDE Projects - Molecule Tracking - Neural Crest Cell Tracking - Quantification of chromatin structure in neurons - - Quantification of dynamic recruitment of repair factors to DNA-Damage - Automated localization, tracking and classification of dividing cells in live cell movies
4 Quantification of dynamic recruitment of repair factors to DNA-Damage Christian Dietz M.Sc. University of Konstanz
5 Dynamic recruitment of repair factors to DNA-Damage Information Nucleus is shot and damaged by a laser Change of intensity over time in the damaged region quantifies the repair-protein A B
6 Dynamic recruitment of repair factors to DNA-Damage Goal
7 Dynamic recruitment of repair factors to DNA-Damage Manual workflow - A lot of work to do... ImageJ helps Manual segmentation of the cell Manual selection of the damaged area Measurement and analysis with different tools (e.g. Matlab, Excel ) Manual workflow - Problems Result is not reproducible Rater-depended But most important: Time consuming!
8 Dynamic recruitment of repair factors to DNA-Damage KNIME Workflow - Overview
9 Dynamic recruitment of repair factors to DNA-Damage Algorithm: Segmentation of the cells
10 Dynamic recruitment of repair factors to DNA-Damage Algorithm: Segmentation of the cells
11 Dynamic recruitment of repair factors to DNA-Damage Algorithm: Cropping and Registration of the nucleus Valid measurement demand non-moving cells
12 Dynamic recruitment of repair factors to DNA-Damage Algorithm: Identifying the damaged part Score (max n ) = Intensity(max n ) + Area gradient(max n ) + CenterDist(max n )
13 Dynamic recruitment of repair factors to DNA-Damage Results (101 Stacks with ~ 62 pictures per stack) Segmentation ~ 99 % correct segmented Damage detection ~ 99 % correct detected
14 Automated localization, tracking and classification of dividing cells in live cell movies Christian Dietz M.Sc. University of Konstanz
15 Automated localization, tracking and classification of dividing cells Movie
16 Automated localization, tracking and classification of dividing cells Phases to classify Interphase Prophase Prometaphase Metaphase Anaphase Telophase t t+1 t+2 t+3 t+4 t+5
17 Automated localization, tracking and classification of dividing cells [Viola et al. 2002] AdaBoost.SIP [Zhang 2009]: Localization, tracking and classification of cells
18 Automated localization, tracking and classification of dividing cells Polar-Masks for feature extraction
19 Automated localization, tracking and classification of dividing cells Localization and classification Sampling Tracking and classification Post processing Sampling the picture
20 Automated localization, tracking and classification of dividing cells Localization and classification Centralization Tracking and classification Adjust the sampling points towards the cell center Post processing
21 Automated localization, tracking and classification of dividing cells Localization and classification Classification and filtering Tracking and classification Classify the sampling points and take the best ones Post processing
22 Automated localization, tracking and classification of dividing cells Sampling Classification Localization and classification Tracking and classification Post processing t t+1
23 Automated localization, tracking and classification of dividing cells Localization and classification Start Tracking and classification Post processing
24 Automated localization, tracking and classification of dividing cells Localization and classification Tracking and classification Post processing Predefined phase transistions
25 Automated localization, tracking and classification of dividing cells Localization and classification Tracking and classification Post processing Dijkstra
26 Automated localization, tracking and classification of dividing cells
27 Automated localization, tracking and classification of dividing cells
28 Automated localization, tracking and classification of dividing cells Advantages - No precise segmentation needed - Classification results improve the tracking - Still many extensions and improvements possible (e.g. reducing number of parameters) Open questions - How to handle dead cells? - Crossing trajectories - Completely overlapping cells Future work - Extension of the post processing - Improvement of the AdaBoost.SIP - Learning trajectories
29 Thank you
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