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

Jan 18, 2008

Outline

• Motivation• Proposed approach• Experimental results• Conclusions

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

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

Jan 18, 2008

Approach

Nuclear segmentation

E-cadherin segmentation

a

b

E-cadherin assignmentc

NucleusE-cadherin

Feature extraction

... ... n dimension

Feature selection

m dimension, m<<n

Discriminant analysis

treatment group #1

treatment group #2

Classfiertraining

testing phenotype

predictedtreatment

label

d

e

f

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

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

Jan 18, 2008

kernel topography for detection of membrane signal

• Bidirectional• Energy dissipates as a

function of distance• Energy becomes more

focused iteratively

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

Nuclear segmentation

E-cadherin segmentation

a

b

E-cadherin assignmentc

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

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)

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)

Jan 18, 2008

Data organization in Biosig database

Jan 18, 2008

Classification between treatment groups

Jan 18, 2008

Visualization of phenotypic responses – density maps

Sham

1GyFe 2GyGamma

Distribution of E-cadherin intensity per cell

Jan 18, 2008

Visualization of phenotypic responses – heat maps

Sham

TGFbeta

0.1GyFe+TGFbeta

0.2GyFe+TGFbeta

0.5GyFe+TGFbeta

1GyFe+TGFbeta

1GyFe

0.03Gygamma+TGFbeta

0.1Gygamma+TGFbeta

0.4Gygamma+TGFbeta

1Gygamma+TGFbeta

2Gygamma

2Gygamma+TGFbeta

Nu

cle

ar

size

Nu

c te

xtu

re 1

Nu

c te

xtu

re 2

Nu

c te

xtu

re 8

Eca

dh

eri

n m

ea

n

Eca

dh

eri

n s

td

Eca

dh

eri

n t

ota

l

Sham

TGFbeta

0.1GyFe+TGFbeta

0.2GyFe+TGFbeta0.5GyFe+TGFbeta

1GyFe+TGFbeta

1GyFe

0.03Gygamma+TGFbeta

0.1Gygamma+TGFbeta

0.4Gygamma+TGFbeta

1Gygamma+TGFbeta

2Gygamma

2Gygamma+TGFbeta

Nuc

lea

r si

ze

Nuc

text

ure

1

Nuc

text

ure

2

Nuc

text

ure

8

Eca

dher

in m

ean

Eca

dher

in s

td

Eca

dher

in t

otal

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.

Jan 18, 2008

Thank you!