Modelling TIRF Microscopy Data of Dynamic Microtubules at Super-Resolution

1. Modelling TIRF Microscopy Data of Dynamic Microtubules at Super-Resolution Summer Project Results Nils Gustafsson Supervised By: Dr Lewis Griffin Dr Thomas Surrey

2. Summary • Microtubules • Microtubules and the Cytoskeleton • Analysis of Microtubule Growth • Modelling of Vitro Microtubule Experiments • “Gaussian Rendering” Approach • Validation of In Vitro Experiments • Accuracy and Precision of End Tracking • Labelling Ratio • Labelling Ratio With Noise • Experimental Conditions • Conclusions

3. Microtubules and the Cytoskeleton Multiple Cellular Functions • Mechanical Stability • Scaffold Structures • Force Generation • Cargo Transport • Cell Migration • Cell Differentiation • Cell Division Drug Targets • Vinca Alkaloids • Taxanes Microtubules (green) DNA (blue) EB1 (yellow) Fig. (top right) The Dixit Lab research webpage, Washington University Fig. (bottom right) taken from Torsten Wittmann homepage, UCSF

4. Analysis of Microtubule Growth

5. Modelling microscope data

6. Projection Onto a 2D Plane

7. Rendering Into an Image

8. Accuracy and precision of tracking algorithm

9. Labelling Ratio Response of the accuracy to labelling ratio is non-linear

10. Labelling ratio in a 1D Toy Model Error in the mean of a cumulative Gaussian fit to a 1D toy model of an imaged microtubule shows that the response of the accuracy to labelling ratio is non-linear

11. Labelling Ratio and SNR Precision is clustered into three groups proportional to SNR value

12. Experimental Conditions Precision is clustered into two groups proportional to SNR value Best Case Axial accuracy <5nm Axial precision <42nm Lateral precision <1nm Lateral precision <14nm

13. Conclusions • A new dynamic microtubule TIRF data simulator has been created • True representation of nanoscale structure • Easy manipulation of orientation and MT bending • Computationally efficient • The accuracy and precision of a microtubule end tracking algorithm has been characterised • Best case axial accuracy is <5nm • Best case axial precision is <42nm • A modification to mean squared displacement theory has been derived in order to make use of the empirically derived accuracy of the end tracking

14. Acknowledgements Microtubule Cytoskeleton Lab, Cancer Research UK • Dr Thomas Surrey  Laboratory Head • Dr Nicholas Cade   Principal Scientific Officer • Dr Iris Lueke   Senior Scientific Officer • Ms Claire Thomas   Senior Scientific Officer • Dr Sebastian Maurer Previous Group Member • Dr Christian Duellberg   Scientific Officer • Dr Jayant Asthana   Research Fellow • Dr Todd Fallesen   Research Fellow • Dr Franck Fourniol   Research Fellow • Dr Johanna Roostalu   Research Fellow • Dr Einat Schnur    Research Fellow • Dr Hella Baumann   Graduate Student • Mr Jonathon Hannabuss   Graduate Student • Ms Rupam Jha   Graduate Student • Mr Gergo Bohner   Diploma Student CoMPLEX, UCL • Dr Lewis Griffin • Ms Stephanie Reynolds

15. Validating End Definition

16. Time in Latice

17. “Best Case” and Spatial Dependence

18. “Best Case” and Spatial Dependence

19. Tip Movement Dependence

20. SNR

21. Exposure Time

22. Taper Length

23. Modelling of Microtubule Dynamics • Accurate quantification of experiment leads to improved models • Models should include: • Growth velocities and fluctuations • Interaction with MAPs • Catastrophe/rescue frequencies Fig. (left) modified from Gardner et. al. Cell, 2011 Fig. (right) modified from Maurer, Cade, Bohner et. al. Current Biology, 2014

24. Analysis of In Vitro Experiments • Custom analysis software tracks end positions • Using convolved model fitting • Sub-pixel precision alignment of frames allows averaged intensity profiles to be produced • Multiple channels can be analysed including MAP structures

25. Dynamic Instability Fig. taken from C. Conde & A. Caceres, Nature reviews Neuroscience, 2009

26. Dynamic Instability • Microtubule (+)end tracking proteins (green) reveal rapid growth and shrinkage episodes in live cells. • Fine control of microtubule dynamics by microtubule associated proteins (MAPs). Fig. (bottom left) taken from Molecular Cell Biology 4th ed, Lodish

27. Simulating Experimental Data • Monte-Carlo Simulation of the 1D model defines a state sequence used to reconstruct microscope images • Considerations: 200-2000 states per frame, noise, movement, labelling densities, magnification…… Fig. (center left) modified from Gardner et. al. Cell, 2011

28. Characterisation of Noise

29. Validation of In Vitro Experiment Analysis • Simple simulations have previously been used to determine resolution of taper lengths • We would like to be able to determine accuracy of tracking of dynamic characteristics of microtubule growth – such as growth fluctuations. Fig. (bottom left) modified from Maurer, Cade, Bohner et. al. Current Biology, 2014

30. Microtubules and the Cytoskeleton Multiple Cellular Functions • Mechanical Stability • Scaffold Structures • Force Generation • Cargo Transport • Cell Migration • Cell Differentiation • Cell Division Drug Targets • Vinca Alkaloids • Taxanes Microtubules (green) DNA (blue) EB1 (yellow) Fig. (top right) taken from Molecular Cell Biology 4th ed, Lodish Fig. (bottom right) taken from Torsten Wittmann homepage, UCSF

31. In Vitro Microtubule Experiments • Stabilised GMPCPP seeds are bound to a cover slip • Fluorescently tagged tubulin subunits are introduced via micro-fluidics • Microtubules are nucleated at the seeds • Imaged by TIRF microscopy as they grow Fig. (bottom left) taken from C Duellberg’s PhD Thesis Fig. (bottom right) taken from The Dixit Lab research webpage, Washington University

32. Labelling Ratio

33. Labelling Ratio and SNR

34. Experimental Conditions

35. Fine Detail Image Constructed

36. Rendering Into an Image