ligand screens: raw and b cells madhu natarajan, rama ranganathan afcs annual meeting 2004

Ligand Screens: RAW and B cells

Madhu Natarajan, Rama RanganathanAFCS Annual Meeting 2004

The first Question of the AfCS:

How complex is signal processing in cells?

Signaling NetworkInputs Outputs

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The first Question of the AfCS:How complex is signal processing in cells?

The purpose of the ligand screen:

(1) classify output responses

(2) determine degree of functional cross-talk between pathways

Signaling NetworkLigands Outputs

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The first Question of the AfCS:How complex is signal processing in cells?

(a) A quantitative measure of similarity or dissimilarity of ligands (this talk)

Signaling NetworkLigands Outputs

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The first Question of the AfCS:How complex is signal processing in cells?

(a) A quantitative measure of similarity or dissimilarity of ligands

(b) Quantitative evaluation of the interactions between pairs of ligand responses, and an estimation of total interaction density. (Rama, Elliott…)

Signaling NetworkLigands Outputs

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The first Question of the AfCS:How complex is signal processing in cells?

(a) A quantitative measure of similarity or dissimilarity of ligands. Remember: We are mapping input-output relationships, i.e., we are relating a measured set of outputs to an input.

Signaling NetworkLigands Outputs

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The first Question of the AfCS:How complex is signal processing in cells?

(a) A quantitative measure of similarity or dissimilarity of ligands. Remember: We are mapping input-output relationships, i.e., we are relating a measured set of outputs to an input.

This may or may not provide much information about specific mechanism.

The goal of the ligand screen is to profile ligands and identify interactions, which leads to bigger and better things.

Signaling NetworkLigands Outputs

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A single ligand screen:

Ligand

The first Question of the AfCS:How complex is signal processing in cells?

(a) A quantitative measure of similarity or dissimilarity of ligands. Questions:

1. How do we combine all the multivariate output data into general parameters that represent signaling?

Signaling NetworkLigands Outputs

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n

1

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The first Question of the AfCS:How complex is signal processing in cells?

(a) A quantitative measure of similarity or dissimilarity of ligands. The issues:

1. A way of combining all the multivariate output data into general parameters that represent signaling.

2. Eliminating data redundancy: Calcium (100s of points), microarrays (thousands). Clearly not all are needed.

Signaling NetworkLigands Outputs

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n

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The first Question of the AfCS:How complex is signal processing in cells?

(a) A quantitative measure of similarity or dissimilarity of ligands. The issues:

1. A way of combining all the multivariate output data into general parameters that represent signaling.

2. Eliminating data redundancy3. A formalism for calculating similarity of responses.

Signaling NetworkLigands Outputs

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Our approach is to make an Gaussian error model for the unstimulated value of each variable. Then convert each variable for a ligand into the statistical significance of observing the value given the unstimulated value and error model.

basal

Observed value

Merging different types of data:A quantitative measure of similarity

basal

Observed value

So, we define a parameter S (for significance or signaling):

basalobserved

S

Our approach is to make an Gaussian error model for the unstimulated value of each variable. Then convert each variable for a ligand into the statistical significance of observing the value given the unstimulated value and error model.

Merging different types of data:A quantitative measure of similarity

2

10

Example: A basal value of 2 and a standard deviation of 0.5, gives us an S-score of 16

5.0

210 S

Our approach is to make an Gaussian error model for the unstimulated value of each variable. Then convert each variable for a ligand into the statistical significance of observing the value given the unstimulated value and error model.

Merging different types of data:A quantitative measure of similarity

Every data element we collect (regardless of type, time scale, method of collection) can now be put on a common basis for comparison, clustering, etc.

The only assumption is that the basal value is normally distributed around its mean.

Our approach is to make an Gaussian error model for the unstimulated value of each variable. Then convert each variable for a ligand into the statistical significance of observing the value given the unstimulated value and error model.

Merging different types of data:A quantitative measure of similarity

basal S

Building a unified experiment space:The structure of the matrix

Calcium cAMP phosphoproteins microarrays

Ligand 1

Ligand 32

Ligand 2...

Time Time Time Time

S-scores S-scores S-scores S-scores

Ligand profile

Building a unified experiment space:Understanding each measured parameter: cAMP

Calcium cAMP phosphoproteins microarrays

Ligand 1

Ligand 32

Ligand 2...

Time Time Time Time

0.5 1 3 208

5 dimensions

S-scores

Building a unified experiment space:Understanding each measured parameter: phosphoproteins

Calcium cAMP phosphoproteins microarrays

Ligand 1

Ligand 32

Ligand 2...

Time Time Time Time

ST6P90AKTER1ER2PKMST3P65JNK1JNKsP38

2.5 5 15 30

44 dimensions5 dimensions

S-scores

Building a unified experiment space:Understanding each measured parameter: calcium, microarrays

Calcium cAMP phosphoproteins microarrays

Ligand 1

Ligand 32

Ligand 2...

Time Time Time Time

15000+ probes@ each time

(80 dimensions)

200 timepoints

(5 dimensions) 44 dimensions5 dimensions

30m 1 2 4

Results: The merged unified experiment space

2MA

40LAIGBAFBLCBOMCGSCPG

DIMELCFMLGRHIL4I10IFBIFG

IGFLB4LPALPSM3ANEBNGFNPY

PAFPGES1PSDFSLCTERTGFTNF

Calciumphosphoproteins microarrays

cAMP

The experiment space

2MA

40LAIGBAFBLCBOMCGSCPG

DIMELCFMLGRHIL4I10IFBIFG

IGFLB4LPALPSM3ANEBNGFNPY

PAFPGES1PSDFSLCTERTGFTNF

Calciumphosphoproteins microarrays

cAMP

cAMP: Each time-point in the experiment space is a separate dimension

2MA

40LAIGBAFBLCBOMCGSCPG

DIMELCFMLGRHIL4I10IFBIFG

IGFLB4LPALPSM3ANEBNGFNPY

PAFPGES1PSDFSLCTERTGFTNF

Time: Left to Right: 0.5, 1, 3, 8, 20 min.

2MA

40LAIGBAFBLCBOMCGSCPG

DIMELCFMLGRHIL4I10IFBIFG

IGFLB4LPALPSM3ANEBNGFNPY

PAFPGES1PSDFSLCTERTGFTNF

minutes

fold

cAMP: S-space notation preserves information

S-space

MeasuredData

Phosphoproteins: Each time-point in the experiment space is a separate dimension

2MA

40LAIGBAFBLCBOMCGSCPG

DIMELCFMLGRHIL4I10IFBIFG

IGFLB4LPALPSM3ANEBNGFNPY

PAFPGES1PSDFSLCTERTGFTNF

15

2.5

30

5

For each timepoint:11 phosphoproteins.

2MA

40LAIGBAFBLCBOMCGSCPG

DIMELCFMLGRHIL4I10IFBIFG

IGFLB4LPALPSM3ANEBNGFNPY

PAFPGES1PSDFSLCTERTGFTNF

Phosphoproteins: Examples

minutes

fold

S-space

Measured

2MA

40LAIGBAFBLCBOMCGSCPG

DIMELCFMLGRHIL4I10IFBIFG

IGFLB4LPALPSM3ANEBNGFNPY

PAFPGES1PSDFSLCTERTGFTNF

Phosphoproteins: Examples

minutes

S-space

Measured

fold

2MA

40LAIGBAFBLCBOMCGSCPG

DIMELCFMLGRHIL4I10IFBIFG

IGFLB4LPALPSM3ANEBNGFNPY

PAFPGES1PSDFSLCTERTGFTNF

Phosphoproteins: Examples

minutes

S-space

Phosphoproteins: Examples

minutes

fold

minutes

fold

Measured

Measured

Calcium dimensions in the experiment space

Calcium

LIG

AN

DS

Two issues:

Experiment to experiment variability.

Dealing with parameterization. Clearly we don’t need all 200+ timepoints

AIG

1. Calcium: Experiment to experiment variability

seconds

Calc

ium

(nM

)

1. Calcium: Amplitude normalization

AIG

seconds

Am

plit

ude r

ela

tive t

o p

eak

An example:

H. Flyvbjerg, E. Jobs, S. Leibler, P.N.A.S 1996, Kinetics of self-assembling microtubules: An “inverse problem” in biochemistry

“Phenomenological scaling” : When feasible? If overall behaviour common to the time series is dominated by a (single) set of mechanisms that can be scaled linearly...

Madhu Natarajan: May 04, 2004

1. Calcium: Time Normalization

Time and Amplitude normalization for calcium responses

T 10T

seconds

Calc

ium

(nM

)

Time and Amplitude normalization for calcium responses

T 10T

seconds

Calc

ium

(nM

)

Time relative to peak

Sca

led C

alc

ium

Time and Amplitude normalization for calcium responses

A similar mechanistic process accounts for the calcium response to LPA despite the difference in size and timing of responses

T 10T 20T 30T

seconds

Calc

ium

(nM

)

Time relative to peak

Sca

led C

alc

ium

The results are not artefactual despite the similarity of calcium profiles

Scaling on the time-axis does not reduce discrimination between ligands

T 10T 20T 30TTime relative to LPA peak

Sca

led C

alc

ium

1. Calcium day-to-day response differences are not “biological”

Experimentson 7 different days

2. Calcium: Dealing with parameterization

Am

plitu

de

Time

Am

plitu

de

Time (kinetics)

A1

An

.

.

.

T1 Tm

. . .

2. Calcium: Dealing with parameterization

Conventional approach

0 600Time (sec)

2. Calcium Data Reduction: A cluster-based approach

Let natural distinctions in the data describe parameters

2. Calcium Data Reduction

Data separation mirrors what we have intuitively been using all along

Results: The merged unified experiment space

2MA

40LAIGBAFBLCBOMCGSCPG

DIMELCFMLGRHIL4I10IFBIFG

IGFLB4LPALPSM3ANEBNGFNPY

PAFPGES1PSDFSLCTERTGFTNF

Calciumphosphoproteins microarrays

cAMP

microarrays

Microarrays

15000+ probes

Need to define distinctions in a large dataset .. in response to a reasonably diverse set of perturbations

Stuart et al. “A gene-coexpression network for global discovery of conserved genetic modules”, Science, October 2003.

Meta-genes: Evolutionary conservation as a criterion to identify genes that are functionally important from a set of co-regulated genes.

2. Microarrays

Stuart et al., Science 2003.

Gene X Gene Y

Gene A Gene B

BLAST

Evolutionary Conservation

Identify meta-genes that show correlation in multiple experimental conditions from several gene expression databanks.

Finally create a co-expression network.

2. Microarrays

“Meta-genes”

2. Microarrays

Stuart et al., Science 2003.

Gene List:

Gene 1Gene 2…

Gene N

correlation=high

Stuart et al., Science 2003.

3-D mapping of meta-genes: Distance between genes is an index of probability of co-regulation.

2. Microarrays

Stuart et al., Science 2003.

3-D mapping of meta-genes: Distance between genes is an index of probability of co-regulation.

2. Microarrays

AfCS data-set:

1. Identify meta-genes within the Bcell data.

2. Identify significantly changing genes.

3. Gene count

Lipid metabolism

Signaling

Translation Initiation & Elongation

Energy Generation

Proteasome

Cell Cycle

General Transcription

Translation Initiation & Elongation

Ribosomal Subunits

Secretion

1 hr30 m 2 h 4 h

Microarrays

2MA

40LAIGBAFBLCBOMCGSCPG

DIMELCFMLGRHIL4I10IFBIFG

IGFLB4LPALPSM3ANEBNGFNPY

PAFPGES1PSDFSLCTERTGFTNF

Microarrays

2 hours

30 min

4 hours

1 hour

For each timepoint:10 functional groups each with 2 gene counts (up, down)

2 hours30 min 4 hours1 hour

Results: The merged unified experiment space

2MA

40LAIGBAFBLCBOMCGSCPG

DIMELCFMLGRHIL4I10IFBIFG

IGFLB4LPALPSM3ANEBNGFNPY

PAFPGES1PSDFSLCTERTGFTNF

Calciumphosphoproteins microarrays

cAMP

Clustering the experiment space: Similarity/dissimilarity between ligands

-20

20

1. A simple transformation of raw data variables into dimensionless S variables (units of significance) permits construction of an unified experiment space of all data, regardless of source or differences in intrinsic dynamic range and signal to noise.

Summary:

1. A simple transformation of raw data variables into dimensionless S variables (units of significance) permits construction of an unified experiment space of all data, regardless of source or differences in intrinsic dynamic range and signal to noise.

2. A potentially serious danger is over-parameterization, the usage of many non-independent variables to represent a biological process (say, the inactivation of a calcium response). We have shown two ways to address this problem on the calcium and microarray dimensions.

Summary:

1. A simple transformation of raw data variables into dimensionless S variables (units of significance) permits construction of an unified experiment space of all data, regardless of source or differences in intrinsic dynamic range and signal to noise.

2. A potentially serious danger is over-parameterization, the usage of many non-independent variables to represent a biological process (say, the inactivation of a calcium response). We have shown two ways to address this problem on the calcium and microarray dimensions.

3. We have assembled an experiment space of 32 ligands by 134 parameters that represents the single ligand profiles for each ligand. This space can be clustered to identify similarities/dissimilarities between ligands.

Summary:

1. A simple transformation of raw data variables into dimensionless S variables (units of significance) permits construction of an unified experiment space of all data, regardless of source or differences in intrinsic dynamic range and signal to noise.

2. A potentially serious danger is over-parameterization, the usage of many non-independent variables to represent a biological process (say, the inactivation of a calcium response). We have shown two ways to address this problem on the calcium and microarray dimensions.

3. We have assembled an experiment space of 32 ligands by 134 parameters that represents the single ligand profiles for each ligand. This space can be clustered to identify similarities/dissimilarities between ligands.

4. The method of analysis is independent of cell type or assays within the ligand screen. It is equally applicable to the B cell or the RAW cell data.

Summary:

1. A simple transformation of raw data variables into dimensionless S variables (units of significance) permits construction of an unified experiment space of all data, regardless of source or differences in intrinsic dynamic range and signal to noise.

2. A potentially serious danger is over-parameterization, the usage of many non-independent variables to represent a biological process (say, the inactivation of a calcium response). We have shown two ways to address this problem on the calcium and microarray dimensions.

3. We have assembled an experiment space of 32 ligands by 134 parameters that represents the single ligand profiles for each ligand. This space can be clustered to identify similarities/dissimilarities between ligands.

4. The method of analysis is independent of cell type or assays within the ligand screen. It is equally applicable to the B cell or the RAW cell data.

Looking ahead: Applicability to the double ligand screen and beyond: Rama, Elliott.

Summary:

Acknowledgements

Rama Ranganathan

Paul SternweisElliott RossRon TaussigMel SimonAl Gilman