Background to Code Development

The method was also adapted to allow the intensity along a microtubule to be extracted

from a second ”channel”, allowing the study of the distribution of a microtubule-associated

protein tagged with a suitably spectrally distant fluorophore. Here a full description of the

resulting code was presented.

A number of questions were raised as to how accurate this method could be. To test this,

and to assist in the optimisation of experimental conditions, synthetic microtubule images

were generated to test a number of experimentally variable parameters. This enabled

an accurate picture of some of the shortcomings of this method, as well as obtaining a

sensible range of error that could be expected.

The method was then applied to image stacks of dynamic microtubules from four different

experiments, which vary by tubulin concentration. This was done to verify the previous

results presented in Gardner et al. (2011), and see whether microtubule tip structure

becomes more elongated with an increase in tubulin concentration. Following this, EB3

was added to dynamic microtubule chambers to see if it has an effect on microtubule

tip structure. EB3 was chosen as it has the highest affinity for the microtubule tip out of

the EB family of proteins. This makes it a prime candidate for use, as it should give the

largest effect on microtubule dynamics.

3.2

Background to Code Development

The code used by Gardner et al. (2011) and presented in Demchouk et al. (2011) to fit

to dynamic microtubules was made publicly available. Briefly, the code allows the user to

select a single image stack. Within that image stack the code allows the user to select a

single microtubule by first clicking at some point along the microtubule lattice and then in

close proximity to the tip. Upon the second click the code fits that time slice, which takes

in excess of 30 seconds. The fit occurs by the y-axis and x-axis swapping if necessary

such that the distance between the two points selected was greatest in y than in x. Scans

are then conducted along y-axis, fitting a Gaussian function to the intensity values for

each column of values for each point in y. This identifies the microtubule backbone. A

3.2. BACKGROUND TO CODE DEVELOPMENT

microtubule. The intensities along the line where then extracted. To these intensities a

Gaussian error function was fitted (Section 3.4.1, Equation 3.3) to find the microtubule

length, and the microtubule taper length. Following fitting to that time point the code then

asks you to click on the microtubule tip in the next time point, or right click to finish.

It was decided that having to click on the end of the microtubule for every time point that

was required for analysis, and having to wait for around 30 seconds for each time point

to be analysed was not very efficient. Whilst a quick solution would have been to return

the position found from the previous time point as the position to fit to in the next time

point, it was decided that it would be good to take this opportunity to recode from an

understanding view point.

A number of other issues were also thought about. Demchouk et al. (2011) only fit a linear

function to the microtubule, in a significant proportion of microtubule images obtained on

our microscope the microtubules are not linear. Being only able to select one microtubule

at a time for analysis also seems to be inefficient. Additionally our aim was to study the

effect of EB (or other MAPs) alongside microtubule dynamics, it might be preferable to

extract the intensity along the microtubule tip from more than one colour channel. It was

assumed that the image from the second channel would be acquired equally between

two consecutive images in the microtubule channel.

From this it was decided that as part of re-coding of the routine, it would also be

redesigned into a user-friendlier package. Additionally improvements would be made

to allow the selection of all microtubules at the start, followed by complete automation in

order to limit the amount of user interaction and therefore input time required by the user.

This has the bonus of allowing pre-processing of stacks to also be carried out as part of

the package. During fitting the location of the microtubule in the previous time point would

be used as the microtubule tip location. Careful consideration would be made to ensure

that the right points are being used. Additionally the microtubule backbone would be

allowed to undergo a small amount of flexing by fitting a non-linear line to the microtubule

tip. The location of the microtubule backbone could be copied to additional image stacks

in order to extract the microtubule intensity from those stacks. The general layout of the

3.2. BACKGROUND TO CODE DEVELOPMENT

image acquisition

x y t

load into MATLAB® _{image stack processing}

create substacks from user inputted ROIs open ImageJ® _{environment in MATLAB}®

to allow manual selection of ROIs

Identification of microtubule backbone microtubule tip estimation 1, 2 used for t+1 fixed point drift calculated

fixed point drift corrected and intensity extracted across all channels

1 2 FP t 1 2 FP t FP results saved FURTHER ANALYSIS optional

Figure 3.1:Schema Overview of Microtubule End Fitting Code: Figure legend continued on the next page.