1 / 18

Cell modelling using the Atomic Force Microscope Gary Johnston

Cell modelling using the Atomic Force Microscope Gary Johnston. Presentation Overview. Who and what – A brief Background Why – Are we doing this? How – are we planning on doing the work? Results – Some recent tentative results. Current Issues - near and not so near. Brief Background.

Download Presentation

Cell modelling using the Atomic Force Microscope Gary Johnston

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Cell modelling using the Atomic Force Microscope Gary Johnston

  2. Presentation Overview • Who and what – A brief Background • Why – Are we doing this? • How – are we planning on doing the work? • Results – Some recent tentative results. • Current Issues - near and not so near.

  3. Brief Background • 2 BSc. Degrees, 1 in Molecular biology and 1 in computing. • A foot in both camps • Predominately a Programming background • Games background PS1, PS2 and PC. • Limited timeframes, limited processing power. • Moved to flight simulation around 2000 – Worked on various projects including Eurofighter and GR4 Tornado.

  4. What – am I doing? • Trying to make sense of the data the biologists provide and build a useful statistical model from it. • The data comes from 2 sources. • The Atomic Force Microscope and the Confocal microscope. Actin stained PNT2 cells in a monolayer AFM Force curve Force vs Indentation

  5. Atomic Force Microscope(AFM) • The AFM in a nutshell • We poke the cells with the stick and measure the force from the cantilever.

  6. Atomic Force Microscope(AFM) • The AFM in a nutshell • The scale we are looking at is around a nanometer. • Very small!

  7. Confocal microscope Confocal Actin stained PNT2 cell from a monolayer • Density Actin • Morphology of the Cell • Relative differences of irradiated Actin.

  8. Why – Are we doing this? Radiation Study. • Study the effects of radiation on cancerous and non-cancerous cells. • In particular the how the cell cytoskeleton changes with dosage. • Rationale -: Since the actin cytoskeleton is thought to play a primary role with force within the cell, any changes should alter the force response for the cell • The two techniques mentioned earlier are used. • Confocal imaging – Distribution, morphology. • AFM - Force response.

  9. How – are we planning on doing the work? The ‘Original’ Hertz Model (Conical) • Collecting all the constants together. • Becomes • Assumptions -: Linear varying E • So if we let a0 = 1... • if d = 2 , force = 4 N

  10. How – are we planning on doing the work? • How do we know it’s not correct. • Below shows the best fit for a hertz curve verses that for a cell. N Force m Distance

  11. How – are we planning on doing the work? • If we presume that the youngs modulus E varies non-linearly. • We come up with an extended hertz model. • Below is the fit for both the original and the extended model. Non-linear extended model. original

  12. How – are we planning on doing the work? • The original hertz model was not acceptable. • The extended hertz model, gives a way of recording the force from a cell with reasonable accuracy over the span for the curve. • The next stage of this work is to use Ansys or MatLab. • To enable me to make a non-linear finite element model. • I’m hoping progress it to a data driven, dynamic model eventually. • Confocal data - used to influence the distribution and overall shape. • Force data - used to model the overall force response of the cell.

  13. Recent results Tentative Initial result of investigation into effect of confluence. N • Result 1 – PC3 Confluent cells are less stiff • Test performed via Mann-Whitney equivalent in Matlab. • 95% likelihood that populations are not from the same sample. IE 95% Likely that they are statistically different. • This result requires more data to confirm that this is indeed a pattern for these particular cells. • To the right the Blue X represents the DAY 1 PC3 cells which are not confluent, whereas the red circles represent the DAY 2 PC3 cells which were confluent. Force/N

  14. Time as a factor for AFM stiffness. • Result 2 – Time appears has no influence the cell stiffness for at least an hour. • The line of best fit has been plotted here and is roughly a straight line, this is based upon the force curves at 3x10-6 m indentation over 60 minutes in this case. • As can be seen the amount of force required doesn’t vary significantly. paras = (2*tan(37.5) ) / (0.75 * pi); Eb = p(3)/paras; k2 = p(2)/paras; k1 = p(1)/paras;

  15. Time as a factor for AFM stiffness. • Result 2 – Eb, K1, K2 appear not to vary with time. This is in line with the result of the previous slide. • Shown here are the variations of EB,K1,K2 over the same interval. • As can be seen the least square line is only one of a number of fits you could do. • There appears to be no difference for cell stiffness over time, which is good as otherwise we’d have to factor time into the AFM results.

  16. Summary • Build statistical model • From AFM and Confocal 3D images. • Look for statistical differences in response to radiation • between non-cancerous and cancerous cells. Current Issues (last as I’m Irish) • In the short term. • Choosing the right tool, Ansys or MatLab for modelling work. At present we are probably going to go for Ansys.

  17. Current Issues (Longer Term) • Comparing populations of cells by Eb,K1,K2. Very complex. • Is it better to compare force populations at specific indentations and interpolate between fixed points?

  18. Finally putting it all together. + Biology Where making random mistakes is common practice. = = The End !

More Related