1. Jan 18, 2008 Ju Han, Hang Chang, Mary Helen Barcellos-Hoff, and Bahram Parvin Lawrence Berkeley National Laboratory http://vision.lbl.gov Multivariate characterization of membrane proteins

2. Jan 18, 2008 Outline Motivation Proposed approach Experimental results Conclusions

3. Jan 18, 2008 Importance of membrane proteins E-cadherin forms adherens junctions between epithelial cells and communicates with the actin cytoskeleton through associated intracellular proteins Loss of E-cadherin —Increased cell motility —Cancer progression and metastasis —Increased resistance to cell death Membrane proteins regulate cell-cell interaction and physical properties of tissues http://en.wikipedia.org/wiki/Cadherin

4. Jan 18, 2008 Multivariate characterization of membrane proteins on a cell-cell basis Why? —Cellular responses are heterogeneous —Hidden variables can be identified —Differential phenotypic responses can be improved Challenge —Variation of foreground and background signals Technical Biological

5. Jan 18, 2008 Approach

6. Jan 18, 2008 Nuclear segmentation Nuclear segmentation provides context for quantifying localization of membrane proteins on a cell-cell basis Challenge —Fluorescent signals of adjacent nuclear regions overlap and form a clump Basic idea —Nuclear geometry is almost convex —At the intersection of the overlapping boundaries, folds (points of maximum curvature) are formed —By grouping folds that are formed by a closed contour, a convex partition can be inferred Steps —Delineate isolated nuclear regions —Partition touching cells by applying a series of geometric constraints

7. Jan 18, 2008 Regularizing E-cadherin signals through geometric voting E-cadherin signal has —Perceptual gaps —Non-uniformity in scale Basic idea —Complete perceptual gaps through iterative voting along the direction of negative curvature maxima Voting? —Design bi-directional kernels to project the feature of interest (e.g., negative curvature maxima) —Refine kernel and apply iteratively

8. Jan 18, 2008 kernel topography for detection of membrane signal Bidirectional Energy dissipates as a function of distance Energy becomes more focused iteratively

9. Jan 18, 2008 Assignment of E-cadherin signals through Evolving fronts Initiates from the Voronoi region of the nuclear mask Optimizes an evolving front where external forces are defined by the gradient vector field [Xu CVPR97] —Gradient vector flow

10. Jan 18, 2008 Multivariate representation Nuclear morphology —Size, aspect-ratio, bending energy of contour Structural information —Texture (first, second, and third order derivatives of oriented Gaussian filters) followed by PCA Localization information —Fluorescent intensity and its derived features A schema embedding a total of 425 measurements per cell, which are registered with the BioSig database

11. Jan 18, 2008 Feature selection and classification Feature selection —Ratio of the determinant of between-class scatter matrix and the determinant of within-class scatter matrix —Take a large value when samples are well clustered around their class means and the clusters of different classes are well separated Validation —LDA (Linear discriminant analysis) classifier —Holdout (half for training and half for testing)

12. Jan 18, 2008 Experimental setup Purpose: —Investigate differences between radiation qualities (e.g., gamma and iron) at equal toxicity levels Design —MCF10A cell culture models —Treated with iron and gamma radiations with different dosage in combination with TGFbeta (mimic an effect of stromal cells on radiation response in tissues)

13. Jan 18, 2008 Data organization in Biosig database

14. Jan 18, 2008 Classification between treatment groups

15. Jan 18, 2008 Visualization of phenotypic responses – density maps Sham 1GyFe2GyGamma Distribution of E-cadherin intensity per cell

16. Jan 18, 2008 Visualization of phenotypic responses – heat maps

17. Jan 18, 2008 Conclusions We have developed a series of computational steps to —delineate cell membrane proteins and associate them with specific nuclei —compute a coupled representation of the DNA content with membrane proteins —evaluate computed features associated with such a multivariate representation —discriminate between treatment groups Multivariate representation of cell-cell phenotypes improves predictive capabilities among different treatment groups, and increases quantitative sensitivity of cellular responses.

18. Jan 18, 2008 Thank you!