I started with the data analysis of my experiment. The data is far from being “good”, we can still extract some information out of it.

First, let’s look at the Intensity data. The intensity, **I**, is measured in 50µs time intervals for a total time of 5 min (300sec). We can therefore plot I(t) vs as a function of time. However, we have 6 million data points (do the math – how many 50µs intervals so you have in 5 minutes). Therefore, plotting it would be a very dense graph. We therefore average the points for each second (20,000 data points) to get 300 points. This can be plotted with ease:

Plot 1 (cell number 6): Intensity (y axis) vs time (sec) (x axis)

This is a fairly good data set, with no large “jumps”. Compare to this one:

Plot 2 (cell number 1): Intensity (y axis) vs time (sec) (x axis)

In this second plot, we have time points with large fluctuations (the increase at time 30sec, the decrease in time 200-270sec). This is probably due to a movement of the cell or internal organs, relative to the FCS volume that we measure. These data points can be cropped out (unless you have too many of them).

Next, we look at the Brightness. The Brightness is a quality of the fluorofore, and should not change much over the course of the measurement. Here is the Brightness plot associated with plot 1:

Plot 3 (cell number 6): Brighness (y axis) vs data segment (x axis)

This is considered a “good”, consistent data set. Note that the data is not show per second, but per segment – the data was segmented to 183 pieces of 2^{15} data points (2^{15} x 50µs x 183 = 300 seconds).

This segmentation is, I assume, helpful for the autocorrelation calculations later. I still need to figure it out.

In plot 4, you can see a peak. This indicates some drastic change. Dependeing on your experiment – it can indicate a significant event, or an artifact (as in my case).

Plot 4 (cell number 1): Brighness (y axis) vs data segment (x axis)

The “bad” segments can be taken out of further calculations, which is what we did.

Next, and most popular use of the FCS, is the autocorrelation curve.

The following curve was fitted to a theoretical two population curve.

Plot 5 (cell number 1): Autocorrelation curve. Circles – data segments. Green curve – Theoretical fitted curve.

Note that the point at the earlier time points, and the last time points, usually do not fit well.

g01 and g02 are the autocorrelation calculations for each population. td1 and td2 represent the mobility of the particles measured. A lower td value indicate faster movement.

Below the autocorrelation curve you can see the “residual” curve – this is the plot of the differnce (“residual”)between the actual data and the theoretical data. If the actual data fits well, the residual plot should be randomly-distributed small flactuations around zero.

I still need to go deeper into the physics and math to get a better understanding of the system and the analysis.

This week, or next week, I’m going to do a tutorial, just with fluorofor in solution. I will learn how to operate the FCS microscope and up to the complete data analysis.