ligand screens: raw and b cells madhu natarajan, rama ranganathan afcs annual meeting 2004
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Ligand Screens: RAW and B cells Madhu Natarajan, Rama Ranganathan AFCS Annual Meeting 2004. Signaling Network. 1. 1. Inputs. Outputs. n. m. The first Question of the AfCS:. How complex is signal processing in cells?. Signaling Network. 1. 1. Ligands. Outputs. n. m. - PowerPoint PPT PresentationTRANSCRIPT
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
1
n
1
m
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
1
n
1
m
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
1
n
1
m
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
1
n
1
m
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
1
n
1
m
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
1
n
1
m
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
1
n
1
m
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
1
n
1
m
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
1
n
1
m
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