1. T HE S CREENERS WERE CREATED BY MAN

2. T HEY EVOLVED

3. A ND THEY HAVE A PLAN

4. Drug Screening Test lots of compounds for inhibition against a target

5. High Throughput Screening

6. What if you do not know the target?

7. Not all targets are known 1. Compound X extracted from Australian Jumping Fungus kills cancer cells. What is its mechanism?

8. Not all targets are known 1. Compound X extracted from Australian Jumping Fungus kills cancer cells. What is its mechanism? 2. A collection of leads has been designed for a particular target, do they have any off-target effects?

9. High Content Screening (image from http://www.encorbio.com/monoclonal/MCA-39C7.htm)

12. Questions 1. Can we profile the biological activities of a library of compounds efficiently? 2. Can we use simple probes of the cell cycle to obtain high-resolution biological information? 3. How do we draw conclusions from the massive amounts of data generated?

13. The Screening Library 6,547 Compounds 58% natural products 21% known bioactives 21% structural diversity

14. The Setup Add Hela cells to wells in 384 well plates Add 20 μM compound Incubate 20 hrs Fix, stain

15. The Fluorescent Probes Hoescht 33342 Labels DNA (DAPI) G1/G2 Immunostain Labels Histone H3 Phospho-Ser10 Mitotis Click Chemistry Labeled nucleotide pulse DNA Replication

16. High-throughput Image Analysis #FeatureDescriptionValue 1AreaCh1Nuclear Area X 1 2PerimCh1Nuclear Perimeter X 2 36 IntenCoocASMCh3 EdU Texture X 36 Image 500 cells/well Analyze 36 features from 3-channel fluorescence images

17. Factor Analysis Assume measured variables are linear functions of common, underlying factors. Reduce dimensionality of data to factors that explain common sample variance

18. Factors represent meaningful phenotypes

19. Compounds can be ranked by activity For each factor: (treated – untreated)^2

20. Compounds can be ranked by activity For each factor: (treated – untreated)^2 Six Factors

21. Compounds can be ranked by activity For each factor: (treated – untreated)^2 Six Factors A.J. Hanson/Indiana Univ.

22. Compounds can be ranked by activity For each factor: (treated – untreated)^2 Six Factors

23. Compounds can be clustered by factor scores

24. Compounds cluster into distinct phenotypic groups

25. Phenotypic similarity correlates with structural similarity Phenotype vector Euclidian Distance 2D Fingerprint (ECFP_4) Tanimoto coefficient

26. Structural similarity does not always correlate with phenotypic similarity

27. A: D2 receptor antagonist B: Binds many GPCRs including D2 dopamine receptor

28. Compounds cluster into distinct phenotypic groups

29. Phenotypic similarity captures fine details of activity

30. Cytotoxic macrocylic hexadepsipeptides

31. Phenotypic similarity captures fine details of activity Cytotoxic macrocylic hexadepsipeptides Cytotoxic macrocyclic nonpeptides

32. Clusters can represent mechanisms of action G1/G0 Arrest Phenotype

33. Clusters can represent mechanisms of action Cardiac glycosides inhibit Na/K pumps G1/G0 Arrest Phenotype Can inhibit translation at high levels

34. Clusters can represent mechanisms of action G1/G0 Arrest Phenotype Steroid Hormones

35. Clusters can represent mechanisms of action Cardiac glycosides inhibit Na/K pumps G1/G0 Arrest Phenotype Can inhibit translation at high levels

36. Clusters can represent mechanisms of action Cardiac glycosides inhibit Na/K pumps G1/G0 Arrest Phenotype Can inhibit translation at high levels Translation inhibitor

37. Conclusions Factor analysis can be applied to reduce a huge amount of microscopy data to interpretable biological phenotypes A small number of probes is capable of detecting fine distinctions in compound activity

38. Caveats Could not connect compounds to individual targets

39. Caveats Could not connect compounds to individual targets Is there even a single target?

40. Acknowledgements Faculty Coaches Brian Shoichet Jack Taunton Jim Wells Student Coaches Noah Ollikainen David Booth Heather Newman “One useless compound is called a placebo, two useless compounds are called a controlled experiment, and three or more become a screening library!”

41. Acknowledgements Faculty Coaches Brian Shoichet Jack Taunton Jim Wells Student Coaches Noah Ollikainen David Booth Heather Newman “One useless compound is called a placebo, two useless compounds are called a controlled experiment, and three or more become a screening library!”

43. All Image Analysis Variables

44. 384-well Optical Plates 2000 HeLa Cells/well Grow overnight Add 20 μM compound Incubate 37 o 20hr EdU pulse, Fix The Setup (image from http://www.encorbio.com/monoclonal/MCA-39C7.htm)

45. Factor Analysis Assume measured variables are linear functions of common, underlying factors. Select factors that explain common variance

46. Different compounds vary in potency Phenotypic Response Metric Distance in factor space from control, untreated cells Select “hits” as compounds with top 5% phenotypic response in both replicates 211 compounds (3%)

47. Phenotype-structure correlation is statistically significant

49. Clusters can represent mechanisms of action Cardiac glycosides inhibit Na/K pumps G1/G0 Arrest Phenotype Can inhibit translation at high levels Translation Inhibitor Another translation inhibitor!

50. “One useless compound is called a placebo, two useless compounds are called a controlled experiment, and three or more become a screening library!” High Content Screening and You