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Ligand Screens: RAW and B cells Madhu Natarajan, Rama Ranganathan AFCS Annual Meeting 2004

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.

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Ligand Screens: RAW and B cells Madhu Natarajan, Rama Ranganathan AFCS Annual Meeting 2004

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  1. Ligand Screens: RAW and B cells Madhu Natarajan, Rama Ranganathan AFCS Annual Meeting 2004

  2. Signaling Network 1 1 Inputs Outputs n m The first Question of the AfCS: How complex is signal processing in cells?

  3. Signaling Network 1 1 Ligands Outputs n 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

  4. Signaling Network 1 1 Ligands Outputs n 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)

  5. Signaling Network 1 1 Ligands Outputs n m The first Question of the AfCS: How complex is signal processing in cells? • A quantitative measure of similarity or dissimilarity of ligands • Quantitative evaluation of the interactions between pairs of ligand responses, and an estimation of total interaction density. (Rama, Elliott…)

  6. Signaling Network 1 1 Ligands Outputs n m The first Question of the AfCS: How complex is signal processing in cells? • 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.

  7. Signaling Network 1 1 Ligands Outputs n m The first Question of the AfCS: How complex is signal processing in cells? • 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.

  8. A single ligand screen: Ligand

  9. Signaling Network 1 1 Ligands Outputs n m The first Question of the AfCS: How complex is signal processing in cells? • 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?

  10. Signaling Network 1 1 Ligands Outputs n m The first Question of the AfCS: How complex is signal processing in cells? • 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.

  11. Signaling Network 1 1 Ligands Outputs n m The first Question of the AfCS: How complex is signal processing in cells? • 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 • 3. A formalism for calculating similarity of responses.

  12. Merging different types of data: A quantitative measure of similarity • 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. s Observed value basal

  13. Merging different types of data: A quantitative measure of similarity • 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. s Observed value basal So, we define a parameter S (for significance or signaling):

  14. Merging different types of data: A quantitative measure of similarity • 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. 0.5 10 2 Example: A basal value of 2 and a standard deviation of 0.5, gives us an S-score of 16

  15. s S basal Merging different types of data: A quantitative measure of similarity • 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. 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.

  16. Building a unified experiment space: The structure of the matrix Ligand profile Calcium cAMP phosphoproteins microarrays S-scores S-scores S-scores S-scores Ligand 1 Ligand 2 . . . Ligand 32 Time Time Time Time

  17. Building a unified experiment space: Understanding each measured parameter: cAMP Calcium cAMP phosphoproteins microarrays Ligand 1 Ligand 2 . . . 0.5 1 3 8 20 S-scores 5 dimensions Ligand 32 Time Time Time Time

  18. Building a unified experiment space: Understanding each measured parameter: phosphoproteins Calcium cAMP phosphoproteins microarrays Ligand 1 Ligand 2 2.5 5 15 30 . . . ST6 P90 AKT ER1 ER2 PKM ST3 P65 JNK1 JNKs P38 S-scores 5 dimensions 44 dimensions Ligand 32 Time Time Time Time

  19. Building a unified experiment space: Understanding each measured parameter: calcium, microarrays Calcium cAMP phosphoproteins microarrays Ligand 1 Ligand 2 30m 1 2 4 . . . 15000+ probes @ each time (80 dimensions) 200 timepoints (5 dimensions) 5 dimensions 44 dimensions Ligand 32 Time Time Time Time

  20. 2MA 40L AIG BAF BLC BOM CGS CPG DIM ELC FML GRH IL4 I10 IFB IFG IGF LB4 LPA LPS M3A NEB NGF NPY PAF PGE S1P SDF SLC TER TGF TNF Results: The merged unified experiment space cAMP Calcium phosphoproteins microarrays

  21. The experiment space cAMP Calcium phosphoproteins microarrays 2MA 40L AIG BAF BLC BOM CGS CPG DIM ELC FML GRH IL4 I10 IFB IFG IGF LB4 LPA LPS M3A NEB NGF NPY PAF PGE S1P SDF SLC TER TGF TNF

  22. 2MA 40L AIG BAF BLC BOM CGS CPG DIM ELC FML GRH IL4 I10 IFB IFG IGF LB4 LPA LPS M3A NEB NGF NPY PAF PGE S1P SDF SLC TER TGF TNF cAMP: Each time-point in the experiment space is a separate dimension Time: Left to Right: 0.5, 1, 3, 8, 20 min.

  23. 2MA 40L AIG BAF BLC BOM CGS CPG DIM ELC FML GRH IL4 I10 IFB IFG IGF LB4 LPA LPS M3A NEB NGF NPY PAF PGE S1P SDF SLC TER TGF TNF cAMP: S-space notation preserves information Measured Data fold minutes S-space

  24. 2MA 40L 2.5 AIG 5 BAF BLC 15 BOM 30 CGS CPG DIM ELC FML GRH IL4 I10 IFB IFG IGF LB4 LPA LPS M3A NEB NGF NPY PAF PGE S1P SDF SLC TER TGF TNF Phosphoproteins: Each time-point in the experiment space is a separate dimension For each timepoint: 11 phosphoproteins.

  25. 2MA 40L AIG BAF BLC BOM CGS CPG DIM ELC FML GRH IL4 I10 IFB IFG IGF LB4 LPA LPS M3A NEB NGF NPY PAF PGE S1P SDF SLC TER TGF TNF Phosphoproteins: Examples S-space fold Measured minutes

  26. 2MA 40L AIG BAF BLC BOM CGS CPG DIM ELC FML GRH IL4 I10 IFB IFG IGF LB4 LPA LPS M3A NEB NGF NPY PAF PGE S1P SDF SLC TER TGF TNF Phosphoproteins: Examples S-space fold Measured minutes

  27. 2MA 40L AIG BAF BLC BOM CGS CPG DIM ELC FML GRH IL4 I10 IFB IFG IGF LB4 LPA LPS M3A NEB NGF NPY PAF PGE S1P SDF SLC TER TGF TNF Phosphoproteins: Examples Phosphoproteins: Examples S-space fold Measured minutes Measured fold minutes minutes

  28. Calcium dimensions in the experiment space Calcium Two issues: Experiment to experiment variability. Dealing with parameterization. Clearly we don’t need all 200+ timepoints LIGANDS

  29. 1. Calcium: Experiment to experiment variability AIG Calcium (nM) seconds

  30. 1. Calcium: Amplitude normalization AIG Amplitude relative to peak seconds

  31. 1. Calcium: Time Normalization 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

  32. Time and Amplitude normalization for calcium responses Calcium (nM) seconds T 10T

  33. Time and Amplitude normalization for calcium responses Calcium (nM) seconds Scaled Calcium Time relative to peak T 10T

  34. T 10T 20T 30T Time and Amplitude normalization for calcium responses Calcium (nM) seconds Scaled Calcium Time relative to peak A similar mechanistic process accounts for the calcium response to LPA despite the difference in size and timing of responses

  35. T 10T 20T 30T Scaling on the time-axis does not reduce discrimination between ligands Scaled Calcium Time relative to LPA peak The results are not artefactual despite the similarity of calcium profiles

  36. 1. Calcium day-to-day response differences are not “biological” Experiments on 7 different days

  37. 2. Calcium: Dealing with parameterization Amplitude Time

  38. 2. Calcium: Dealing with parameterization A1 Amplitude Conventional approach . . . An . . . T1 Tm Time (kinetics)

  39. 2. Calcium Data Reduction: A cluster-based approach Let natural distinctions in the data describe parameters 0 600 Time (sec)

  40. 2. Calcium Data Reduction Data separation mirrors what we have intuitively been using all along

  41. 2MA 40L AIG BAF BLC BOM CGS CPG DIM ELC FML GRH IL4 I10 IFB IFG IGF LB4 LPA LPS M3A NEB NGF NPY PAF PGE S1P SDF SLC TER TGF TNF Results: The merged unified experiment space cAMP Calcium phosphoproteins microarrays

  42. 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.

  43. 2. Microarrays Evolutionary Conservation Meta-genes: Evolutionary conservation as a criterion to identify genes that are functionally important from a set of co-regulated genes. BLAST Gene X Gene Y Gene A Gene B Stuart et al., Science 2003.

  44. 2. Microarrays 2. Microarrays Gene List: Gene 1 Gene 2… Gene N “Meta-genes” Identify meta-genes that show correlation in multiple experimental conditions from several gene expression databanks. Finally create a co-expression network. correlation=high Stuart et al., Science 2003.

  45. 2. Microarrays 3-D mapping of meta-genes: Distance between genes is an index of probability of co-regulation. Stuart et al., Science 2003.

  46. 2. Microarrays 3-D mapping of meta-genes: Distance between genes is an index of probability of co-regulation. AfCS data-set: 1. Identify meta-genes within the Bcell data. 2. Identify significantly changing genes. 3. Gene count Stuart et al., Science 2003.

  47. Microarrays Signaling Energy Generation Translation Initiation & Elongation Proteasome Cell Cycle General Transcription Translation Initiation & Elongation Ribosomal Subunits Secretion Lipid metabolism 30 m 1 hr 2 h 4 h

  48. 2MA 40L 30 min AIG 1 hour BAF BLC 2 hours BOM 4 hours CGS CPG DIM ELC FML GRH IL4 I10 IFB IFG IGF LB4 LPA LPS M3A NEB NGF NPY PAF PGE S1P SDF SLC TER TGF TNF Microarrays 30 min 1 hour 2 hours 4 hours For each timepoint: 10 functional groups each with 2 gene counts (up, down)

  49. 2MA 40L AIG BAF BLC BOM CGS CPG DIM ELC FML GRH IL4 I10 IFB IFG IGF LB4 LPA LPS M3A NEB NGF NPY PAF PGE S1P SDF SLC TER TGF TNF Results: The merged unified experiment space cAMP Calcium phosphoproteins microarrays

  50. Clustering the experiment space: Similarity/dissimilarity between ligands 20 -20

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