Flow Data Analysis Challenges Deck from Amgen Attendees

1. Flow Data AnalysisChallenges Deck from Amgen Attendees Wednesday, September 20, 2006 Bioinformatics/ Biostatistics Molecular Computational Biology Sciences John Gosink Cheng Su Katie Newhall Hugh Rand Bill Rees Mark Dalphin Gary Means

2. Sample and meta-data tracking can be complicated 1,000 – 10,000 10 – 100 5 – 10 10,000 – 100,000 5 – 20 blood samples stimulations / sample cell types/mix cell events/ cell type channels/cell FCS files Multiple cell types Blood samples Cell events Stimulation/inhibition combinations Misc drugs Misc cytokines Approx. the size of an Affymetrix Microarray .CEL file An FCS file 5,000 samples x 50 stims/sample x 7 cell-types/cocktail x 5 Mbytes/FCS file @10 terabytes Need a relational database and associated code infrastructure John Gosink, Bioinformatics/Computational Biology, Amgen

3. Some meta-data that we need to capture, store, and index (let alone the actual FCS files/data) • Sample meta-data • Sample ID • Sample to well mapping • Stimulation conditions • Dilutions • Reagent meta-data • Reagent batches • Labeling scheme • Machine meta-data (FCS format currently captures most) • measurement windows • PMT settings • compensation (and matrix) • transformation • Gating parameters • coordinates • thresholds • gate hierarchy John Gosink, Bioinformatics/Computational Biology, Amgen

4. More interesting questions involve natural cell populations and their variation • Catalog of all cell types • What are their distributions in all of flow parameter space • How to standardize between samples and runs • What are fruitful approaches to characterizing these distributions • Baseline catagorization • Number of “typical” cell volumes (archetypes) • Location of archetypes • Shapes of archetypes • Relationships of cell counts in the archetypes • Characterization of the “void” • How empty is the void • How smooth is the void • Detection of novel (sub) populations and unforseen changes John Gosink, Bioinformatics/Computational Biology, Amgen

5. Separation of Overlapping Peaks • Question: How do we best quantitate multiple overlapping peaks • One Approach: Fit peaks as a sum of small numbers of basis set functions. • Issues: Basis set choice, sensitivity, accuracy, … Hugh Rand, Bioinformatics/Computational Biology, Amgen

6. Example histograms Overlap Noisy Small Peaks More Overlap Shape Hugh Rand, Bioinformatics/Computational Biology, Amgen

7. Receptor Occupancy Assay and Analysis FlowCytometer Unlabeled Ab @0 – sat’d dose LabeledAb Labeledanti-recpt Ab Labeledisotype ctrl No Drug in Animal Some Drug in Animal Cell with specificand non-specific receptors.Ab induces more recpt. Cell with specificand non-specific receptors Unlabeled Drug AbLabeled Drug AbLabeled Recpt AbLabeled Isotype Ctrl Ab Mark Dalphin, Bioinformatics/Computational Biology, Amgen

8. Some math… Simple form, without non-specific binding Add non-specific binding and things are not so tidy Mark Dalphin, Bioinformatics/Computational Biology, Amgen

9. Problems with receptor occupancy assays • Even with 1:1 conjugates, MFI varies significantly from Ab to Ab against the same receptor • “Can’t see less than 1,000 receptors per cell” • Large variability from instrument to instrument and run to run • Why doesn’t this behave like a well-controlled physical experiment; why is it “semi-quantitative”? • I’d like to see: • Easy loading of data-sets and meta-data • Module to compute occupancy • Some way to look at associated binding curves Mark Dalphin, Bioinformatics/Computational Biology, Amgen

10. Gating Sensitivity • If gates change slightly, will results change? • Reasons for considering gating sensitivity: • Quantitative analysis of the responses • Gating is done per individual samples • Gating is somewhat subjective, even auto-gating • Multiple gates used • Subgroups of small size Cheng Su, Biostatistics, Amgen

11. Gating Sensitivity Analysis • Sensitivity Analysis • Get new gates by moving the boundary of gates • Conduct analysis • Compare the results • Challenges • software/system: to import the gate boundary • methodology: methods to automate gate movement • and compare results Cheng Su, Biostatistics, Amgen

12. System Outline Samples LSRII B Cells Gating T Cells XML FCS NK Cells Analysis (R,SAS,Java,…) Check against Result Cheng Su, Biostatistics, Amgen

13. How to move what we do in proprietary graphical tools into a more high-throughput environments? • Question: Are there applications available that can accommodate the size of FCS files that I generate, allow me to compare data across a plate, and provide data output in an acceptable format? • Problem: Currently using a 9-color, 12-parameter antibody panel in whole blood (and it’s only getting bigger!) • FCS file size = 10,000 to 30,000 KB • Analysis time = 8 hours for 32 samples/wells • Export time = 20-30 minutes for 32 FCS files • Output = at least 7 gated files for each FCS file Katie Newhall, Molecular Sciences, Amgen

14. How to move what we do in proprietary graphical tools into a more high-throughput environments? • Potential solutions • Analysis • Automated gating • Sample flagging • Comparison of samples across a plate • Output of histogram statistics in an excel format • Export time • Gating information and experimental metadata exported with FCS/TXT files Katie Newhall, Molecular Sciences, Amgen

15. immunophenotyping • experiment: • 80 clinical whole blood samples • no ex vivo manipulation • 4 dose cohorts • 38 3-color, RBClyse/no-wash stains • 3280 6-parameter FCS files • What populations of events change in some way as a function of drug dose or disease state or changes in other populations? Bill Rees, Molecular Sciences, Amgen

16. An immunophenotyping panel T cells B cells NK cells monocytes Bill Rees, Molecular Sciences, Amgen

17. Immunophenotyping • I will not deal with this 2-dimensions at a time • time • too many populations in each stain, only some do I know to look for • don’t know what I’m looking for with minimal biological insight • Issues: • definitions of terms • Metrics, e.g. MFI and %CD45+ events, % responders • Linking raw data to other study data/protocols and to analysis product • Autogating with visual QC • Can the identification of the major cell types (operationally defined by robust stains, e.g. CD3+ CD8+ CD56-) be automated to incrementally reduce the analysis time? Bill Rees, Molecular Sciences, Amgen

18. Whole blood stimulation assays where leukocytes are evaluated for phosphoprotein pathway activation inhibition Gary Means, Molecular Sciences, Amgen Note: This is the region where notes could be placed

19. Process Cells Labeling Flow Sample Data File Soft-ware Whole blood Stimulate metadata Problem? Each set of gated data must be independently exported and kept linked to the experimental process Lymphocyte Granulocyte Monocyte NK B cell T cell Use bioinformatics tools to evaluate coordinate regulation of at multiple different intracellular targets CD4+/CD8+ CD4+ CD8+ DN 11 gates x 4 targets x 96 wells CD4+ memory CD8+ memory Gary Means, Molecular Sciences, Amgen

20. Solutions? • Automatically export events with additional columns which contain all of the gating information associated with each event. • Metadata must be inextricably associated with the experimental results. Gary Means, Molecular Sciences, Amgen