The document discusses using high content screening to quantify cell morphology under different drug conditions and concentrations over time. By measuring 64 morphology parameters for each cell, the system can identify significant features that discriminate cell conditions and cluster cells into different morphology types. Transition models are then built to understand how cell states change in response to perturbations as a way to gain insights into cell function and behavior.

1. High Content Screening as a Tool for Systems Biology James G. Evans Ph.D Assistant Director, Whitehead MIT BioImaging Center Computational & Systems Biology Initiative Massachusetts Institute of Technology

2. Model Mine Manipulate Measure Discovery-driven Hypothesis-driven The Systems Biology Research Paradigm Find high-hanging fruit Understand “failure” Personalized medicine Drug Discovery

3. Cell Motility is Coordinated Multi-component Chemical and Mechanical Process IC-21 murine peritoneal macrophage phase contrast, 30 sec timelapse Podosome-rich zone of adhesion and extension Model Manipulate Mine Measure

4. Measurements build Models of Adhesion and Cell Motility in 3D Matrices Mhz slide Zaman et al, Biophys. J. 2005 Model Manipulate Mine Measure

5. … By Altering Adhesion Complex Turnover untreated demecolcine (1  M 30mins) paclitaxel (10  M 60mins) untreated demecolcine paclitaxel 14 12 10 8 2 6 4 0 Lifetime (mins -1 ) Podosome simple daughter total Podosome interactions fission fusion 2.5 2.0 0.5 0 1.5 1.0 Rate per min -1 Model Manipulate Mine Measure

6. potential information content High Content Screening Model Manipulate Mine Measure

7. Experimental Design and Data Output 1152 ‘conditions’ Drugs (6) nocodazole dmso demecolcine latrunculin A paclitaxel jasplakinolide Concentration (48) 16 attoM – 1 microM Time (4) 1, 10, 100, 1000 minutes Duplicate wells per plate 48 concentrations x2 Five experimental repeats 24 plates x5 Images 3 channel images per field 55 fields per well 5280 fields per plate 633600 fields 1.9M images (3.8TB) Data 13.8M objects 67 cell-level features 132 well-level features 12 housekeeping features 13.8M rows x 210 columns (47GB) Model Manipulate Mine Measure

8. HCS Image Acquisition Focus, Offset, then collect 3 Channels 3 micron Offset Ch1 CMFDA Ch2 Hoechst 33342 Ch3 Texas Red Phalloidin Model Manipulate Mine Measure

9. Arrayscan VTi Images 20x field 523 x 523  m 512nm per pixel Model Manipulate Mine Measure

10. Morphology is Highly Variable within a Population 20x field 0.5  m per pixel Hoechst CMFDA Actin Model Manipulate Mine Measure

11. Build a Database of Morphologies using Snapshots Model Manipulate Mine Measure

12. Correlation of 64 different Morphology Parameters Model Manipulate Mine Measure

13. Significant features have a high KS Z score which are able to discriminate cells under different conditions Take the mean Z score per feature over all drugs and durations Identify and Rank Significant Features Model Manipulate Mine Measure Feature T=1 Rank T=10 Rank T=100 Rank T=1000 Rank ShapeP2ACh1 11 4 1 2 ShapeBFRCh1 13 5 5 3 FiberLengthCh1 15 13 6 5 FiberWidthCh1 1 1 3 4 ConvexHullAreaRatioCh1 10 3 2 1 ConvexHullPerimRatioCh1 8 2 4 7 MemberAvgAreaCh2 3 3 2 2 MemberAvgEqCircDiamCh2 4 4 3 3 MemberObjectAreaRatioCh2 1 1 4 1 MemberObjectAreaDiffCh2 2 2 1 4 SpotFiberCountCh3 3 4 2 4 SpotFiberTotalAreaCh3 2 1 1 2 SpotFiberAvgAreaCh3 1 2 3 1 FiberAlign1Ch3 4 3 4 3

14. all cells Ch1 parameters Ch2 parameters Ch3 parameters morphology “ 1 0 2 ” Compartment-based Clustering shape clusters nuclear clusters actin clusters Model Manipulate Mine Measure

15. Multivariate Properties of each Cluster Model Manipulate Mine Measure

16. Composite Morphology Clusters Ellipse Multi Round Fragments Small Pork Chop Big fan Big String Fragments Round Average Spotty Few big None Low

17. 228 229 Multi-Nucleate, Round Cell and Spotty Hoechst CMFDA Actin

18. 072 073 Ellipse, Pork Chop and Few Big or Spotty Hoechst CMFDA Actin

19. Establish Cell States within a Population Model Manipulate Mine Measure

20. ‘ Cell States’ may be transient and interconnected 10x field 15min interval 1.2  m per pixel CMFDA Model Manipulate Mine Measure

21. Mine Database Morphologies with Live Analyses Model Manipulate Mine Measure

22. 5-15  m/hr 15-30  m/hr 30-45  m/hr apoptosis invasive HIGH concentration SHORT duration drug LOW concentration LONG duration Build Predictive Network Models of Cell Function Model Manipulate Mine Measure

23. T=1 T=2 T=3 T=12 T=4 Cell 1 Cell 2 Calculate Transition Probabilities and Probabilities of a morphology change Method similar to DNA sequence modeling where ATGC are replaced by morphologies - though much larger number of states and more complex networks here Compare Transition NxN matrix between control and drug treatments Markov Chain Models Model Manipulate Mine Measure

24. Future Molecular resolution - signaling state, perturbation using RNAi Constrain cell shape using surface patterning [BioForce] Embryonic stem cells and intestinal adult stem cells Integrate with other data sources

25. Whitehead MIT BioImaging Center Victor Horodincu Victor Chest Brian McKenna Monica Chu Al Davis Muhammad Zaman (now at UT-Austin) Maté Biro (now at MPI-Dresden) Winston Timp (now at Johns Hopkins) Paul Matsudaira (N.U.S. from Jan ‘09) MIT Sloan School Alex Samarov Rajiv Menjoge Roy Welsch Nanyang Technical University Alvin Ng Jagath Rajapakse