Discovery of (new) phenotypes by dynamical modeling of live HeLa cell microscopy data

1. Discovery of (new) phenotypes by dynamical modeling of live HeLa cell microscopy data Gregoire Pau, Wolfgang Huber, EMBL-EBI Cambridge gregoire.pau@ebi.ac.uk

2. Experimental setup • Live cell time-lapse imaging • Genome wide assay • HeLa cell line expressing H2B GFP • Seeded on siRNA spots and grown during 48h • Fluorescence time-lapse imaging (sampling rate=30mn) • Experimental output • 450 chips (including replicates) • 384 spots/chip • Each spot produces a video sequence of 96 images • ~ 200 000 spots ! • More than 200 000 video sequences to annotate/process !

3. Examples No phenotype : normal cell growth Mitotic arrest: accumulation of cells blocked into metaphase, followed by apoptosis Mitotic shape: accumulation of bi-nucleated cells Apoptotic: accumulation of apoptotic cells Control spots Empty spot  No phenotype Scramble siRNA  No phenotype Eg5 siRNA Mitotic arrest INCENP siRNA Mitotic shape bCOP siRNA Apoptotic Phenotype examples

4. No phenotype • No phenotype

5. Mitotic arrest • Mitotic arrest

6. High-throughput problem • Too many spots to annotate (>200 000) ! • How to automatically determine a spot phenotype given its video sequence ? • Proposed method: • Classify every cell in each image • Establish time course cell populations curves • Fit the curves to a realistic parametric model • Resulting parameters can be used for phenotype classification or novelty detection

7. Cell phenotypes not to be confused with a spot phenotype ! • Defined on a cell level • Cell classification is performed in two steps: • Image segmentation • Cell supervised classification using SVM

8. Cell populations time courses

9. Model • Temporal dynamics of cell state change on population average level • Non-linear ODE model

10. Parameters • 11 parameters • 7 kinetic parameters [cells/h] • 4 initial conditions • Robust estimation • Population level • Least square Levenberg-Marquardt fitting • u is the cell proportion that can undergo mitosis • Biological significance

11. Fitted examples

12. Expected parameters for known phenotypes No phenotype • No phenotype: high k3 & low k2 • Mitotic arrest: low k3 & low k5 • Mitotic shape: high k5 • Apoptotic: high k2 Mitotic shape Mitotic arrest Apoptotic Marginal distributions

13. LDA parameters projection

14. Parameters • Each spot is now modelised with 11 parameters • What can we do with them ? • Phenotype classification • 'Automatic phenotyping' • Supervised classification using a known set of spots • Example: detection of mitotic defect phenotypes • Novelty detection • Detecting phenotypes 'far away' from known ones

15. Detecting mitotic defect phenotypes • Using supervised classification • Trained SVM with ~4000 samples • Spot mitotic score = distance to the SVM hyperplane • Gene score = minimum siRNA score (median spot score) • Classification performance • Given a manual testset of 224 mitotic and non-mitotic 666 genes • Sen=0.71, Spe=0.94

16. Ranking genes by mitotic score • Ranking genes

17. Novelty detection • Automatic determination of 'Out-of-model' phenotypes • Spots with a high fitting error • Detection of : • Artefact spots • Local out-of-focus spots • Spots that contain motionless cells Artefact Out-of-focus Motionless

18. Out-of-focus spot • Out of focus

19. Motionless spot • Motionless

20. Novelty detection • Digging for new phenotypes • Looking for a high k6 & reproducible phenotype • 'Apoptotic bi-nucleated cells' • 'Decreasing bi-nucleated cell population' • Short list of 263 spots • Looking for reproducible sirnas • Hit: 125491 flj12436 No phenotype Mitotic shape Mitotic arrest New phenotype ?

21. New phenotype (bi-nucleated apoptotic cells) • flj12436

22. Conclusion • Automatic phenotyping of microscopy time-lapse data • Biologically significant & robust approach • Automatic classification of mitotic defect phenotypes • Good performance compared to manual annotation • Detection of new phenotypes • Out-of-model ones • New ones