Unveiling Cell Migration: New Methods and Insights

1. New methods to study motile phenomena A progress report

2. Topics • The challenge of understanding cell migration • Use of photoactivation & CALI to perturb cell migration • CMAP, a systems biology tool

4. How do we approach a quantitative understanding of cell movement? Top down modeling of integration of collective molecular mechanisms e.g. protrusion, contraction etc. Build up from molecular mechanisms

5. “Up close the paintings of Renoir & Monet look like ‘daubs of paint’, nothing more. Yet when we step back from the canvases, we see fields of flowers” From Davidson’s review of A Different Universe by Robert Laughlin-NYTimes 6/19/05

6. Philosophy of quantitative modeling • Use model to simulate behavior & compare to experiment • Revise model until concrete insight gained into key factors determining migration • Test alternate models • Overarching goal: Quantitatively organize information & ideas on migration mechanisms

7. Advantages of Simple-shaped Cells for Biophysical Studies • Amenable to modeling • Simple shape & migratory pattern: easy to see results of perturbation • Simple, symmetric net traction stress pattern

8. Gliding Fish Keratocyte In the keratocyte, protrusion & retraction smoothly coordinated

9. Keratocyte Doing the Limbo

10. Magnitudes of cell forces

11. Gliding Fish Keratocyte In the keratocyte, protrusion & retraction smoothly coordinated

12. Rxn-diffusion sub-model (simplified) PF-ATPactin TB4-ATPactin cofilin PF-ADPactin polymn Dynamic Network Contraction depolyn MyosinII F-actin

13. A virtual keratocyte--A. Mogilner et al, UC-Davis [Front-dendritic nucleation; rear-dynamic network contraction] Density of f-actin plotted Rubenstein et al SIAM J. 3:413 (2005)

14. Test robustness of in silico models of migration i.e. do we have the rules of integration of protrusion, retraction and adhesion correct? Use light-directed methods to perturb molecular activities in single migrating cells in a spatially & temporally defined way--complement to genetic perturbations

16. Photoactivate or laser inactivate different ABP Concentration of players as numerical input to Mogilner model G, F-actin, Tb4, profilin etc. PF-ATPactin polymn TB4-ATPactin capping protein 2 3 1 Release or inactivate at different points in cell Provide simultaneous traction & network dynamics maps with photoactivation/CALI operation

17. Experimental Perturbation Local ACTIVATION of molecule: photoactivation Local INACTIVATION of molecule: CALI, photoactivation Thymosin -4 Cofilin FAK peptides -actinin Connexins Aurora B kinase Mena Capping protein

18. CALI: Chromophore Assisted Laser Inactivation [Dan Jay]

19. Chromophore Assisted Laser Inactivation (CALI) Light-mediated loss-of-function tool High spatial resolution • Subcellular inactivation • High selectivity High temporal resolution • Instantaneous inactivation • Eliminates genetic/molecular compensation

20. Laser Reactive oxygen species Loss of function x- x- CALI Mechanism Protein damage Chr protein Cell • Chromophore excitation leads to production of free radicals • Free radicals are highly destructive, causing protein damage • - short half-life (nm destruction radius) • Potential for local, instantaneous inactivation of adjacent protein

21. EGFP as a CALI Chromophore EGFP • Advantages • Genetically encoded • Covalent linkage to protein of interest insures specificity • Widely used • Disadvantages • Photostable Ineffective ROS generator • 200-1000X less efficient than other dyes Photostability may also be an advantage in that there are separate regimes for imaging and inactivation

22. CALI of EGFP-Capping Protein Eric Vitriol, Andrea Utrecht & Jim Bear

23. Mena, Capping Protein, and the Regulation of Actin Structure Mejillano et al. 2004

24. CPß Knockdown exhibits more filopodia: can CALI reproduce this phenotype? Control CPß KD Mejillano et al. 2004

25. 5.0 U6 Promoter 5’ LTR Promoter EGFP Capping Protein LENTIVIRUS KD / RESCUE CONSTRUCT TO REPLACE ENDOGENOUS CP WITH EGFP-CP -select clones for good KD& rescue to physiological levels Jim Bear + Andrea Utrecht

26. DIC (left panels) and fluorescence of EGFP-CP (right panels) before (above) and after CALI (below)

27. CALI of EGFP-CPß DIC-pre Pre-flour. Post-fluor Post-DIC <--Large CALI Region

28. F-actin & barbed end increase after CALI-induced dissociation of EGFP-CP from barbed ends of actin filaments Phalloidin stain for f-actin Barbed end assay

29. CMAP: The Causal Map Can the cell biologist’s scheme, which organizes elements, be transformed to a graphical model to check whether it semi-quantitatively predicts observed behavior? Gabriel Weinreb, Maryna Kapustina, Nancy Costigliola& Tim Elston

30. Cell oscillations induced by depolymerizing MT during cell spreading depend on elevated Rho activity and cyclic Cai2+ Pletjushkina et al, Cell Mot. & Cytoskeleton, 48 (4): 235-244 (2001).

31. Spreading mouse fibroblasts with depolymerized MTs Note blebbing-> See also Paluch et al, BJ 89: 724 (2005) &Salbreux et al, Phys Biol 4:268(2007)

32. Quantitation of oscillatory behavior Inactivation of ROCK [arrow] by Y27632 blocks oscillations Control cell spreading

33. Ca2+ also oscillates with similar period as morphological oscillations

34. B. periodic increment due to [Ca2+]i variations increment due MT depolymerization contractility normal spreading time How Rho and Ca2+ may be involved in regulating oscillations

35. External [Ca2+] Cai2+ ↑ MICROTUBULE DEPOLYMERIZATION MLCK↑ P MLC-phosphatase P MLC- ↑ Activate SAC GEF CICR Rho↑ [Ca2+]↓ by retrieval ROCK↑ Adhesion strength CaM Substrate stiffness CONTRACTILITY↑ MORPHOLOGICAL OSCILLATIONS Functional map for cell oscillations depicting necessary elements and connections between them.

36. A systems biology test bed: Experimental readout: % Cells Oscillating, Amplitude, and Period of oscillations CMAP (semi-quantitative) Differential Equation model (quantitative)

37. Complexity Complexity Cognitive networks Boolean networks Petri networks ● ● ● ● ● C M A P ODE, PDE & Stochastic Models Fine-grained models Coarse grained models

38. Causal Mapping [CMAP] • Concepts (elements) are enclosed in boxes and embody chemicals and/or mechanics • Causal influences are edges and enable propagation of causality • Concepts & influences are given numerical or linguistic weights based on data and/or expert opinion

39. W AB B A W BA • A,B are elements of the map, called ‘concepts’. • Wij are the weights (magnitudes) of causal influence of one concept on the other; • [weights are in terms of lingustic variables I.e. very strong….weak that are translated to numerical intervals between -1 to 1] • Positive weight leads to increase of the concept it is directed to (activation) • Negative weight leads to decrease of the concept it is directed to (inhibition). • Time evolution described by simple “transfer” functions that connect concepts & • incorporate weights from one or more input concepts

40. Development of a CMAP for cell oscillations Microtubule depolymerization Biological background: RhoA pathway • Actomyosin based contractility • Volume oscillations • Ca2+ oscillates • Rho pathway involved. CMAP Weinreb, Elston, and Jacobson. 2006

41. CMAP simulation results red=contractility blue=[Ca2+]i MT depolym MT depolym +ROCK inhibition

42. Using the CMAP for hypothesis generation: how do we determine the most likely CMAPs for the phenomenon? Weinreb et al, in preparation What system configurations provide viable hypotheses?

43. W >0 Configuration 1: AB B A activation inactivation W <0 BA W <0 Configuration 2: AB B A inactivation inactivation W <0 BA W =0 Configuration 3: AB B A no influence inactivation W <0 BA

44. Membrane Membrane SAC SAC Cai2+ Cai2+ Ca-pump Ca-pump CONTRACTILITY* CONTRACTILITY* Ca-CaM Ca-CaM MLC-P-ase MLC-P-ase P P MLCK MLCK MLC- MLC- - feedback + feedback Two distinct configurations

45. Algorithm for hypotheses generation • Define experimentally observable criteria that characterize the phenotype: • -oscillatory behavior in [Ca] and contractility • -increasing myosin light chain phosphatase damps oscillations • Determine all possible configurations of the network, i.e. all combinations of possible connections between the elements • For a each configuration, use all possible combinations of weights, Ntotal, [Monte Carlo] and count those that satisfy the criteria, Ni. • Calculate the fitness index as a ratio fi=Ni/Ntotal in order to rank hypotheses [a zero fitness configuation is not a viable hypothesis]

46. Membrane Membrane SAC SAC Cai2+ Cai2+ Ca-pump Ca-pump CONTRACTILITY* CONTRACTILITY* Ca-CaM Ca-CaM MLC-P-ase MLC-P-ase P P MLCK MLCK MLC- MLC- HI FITNESS ZERO FITNESS

47. How can the competing, high fitness hypotheses be experimentally distinguished?

48. Protocol (under development) • Identify on the CMAP a causal influence (weight) which can be experimentally manipulated. e.g. titration of an inhibitor . • Vary the CMAP weight corresponding to the experimental manipulation keeping all other weights in the ensemble of hypotheses (Ni) unchanged. • Examine how system responds to varying the weight of interest. • Compare experimental outcome to prediction of CMAP for different hypotheses. Look for major qualitative differences.

49. Membrane tension SAC MLC-phosphatase Cai2+ Ca-pump CONTRACTILITY Ca-CaM P MLCK MLC- A CMAP for cell oscillations

50. Comparison of experiment & CMAP predictions Hypothesis 5 Experiment Hypothesis 4 Single cell behavior in both experiment & CMAP predictions can also be compared