Today (January 22nd), I went to RPI for another day of interesting work! Today, I analyzed the data from the experiment I was introduced to on December 11th. To summarize the experiment, we were analyzing the size of particles that can normally pass throught the blood brain barrier, using a set up with two wells, one inside of the other (seen below). Twenty wells were tested. Ten had the dextrans added to the apical (inner) chamber, two each for the five different dextran solutions (4 kDa, 10 kDa, 20 kDa, 40 kDa, and 70 kDa). The other ten had the dextrans added to the basolateral (lower) chamber, two each for the five different dextran solutions. Further explanation of this experiment is given in my blog post from December 11th.
The data from this experiment came from solution samples taken at various times over a 25 hour time period. These samples were then scanned and analyzed according to their fluorescent signal. The dextran molecules contained Fluorescein Isothiocyanate (FITC), so the more fluorescnet signal the sample had, the greater the concentration of dextrans in that sample.
To analyze the data, I copied the data from the scan program into a program called Origin. There were three separate scans, so I had to average the data from the three. I then plotted the data for Concentration vs. kDa. After detecting errors in the beginning and end of the graphs, I trimmed the graphs to get a more accurate linear fit. After trimming the graphs, I calculated the r-squared values for each kDa. 4 kDa had by far the best fit. 10 kDa had the worst, and then the r-squared values increase for the 20, 40, and 70 kDa. After examining the Concentration vs. kDa data, I plotted the data for Concentration vs. Time for each of the 20 wells. For every well, the concentration increased over time. This is because as the time increases, more of the cell monolayer dies off, and more dextran molecules are allowed through the barrier. From the starting concetration values, it was evident that the larger molecules started with a lower permeability.
Overall, I had a very interesting day of analyzing data! I can't wait to see what's coming next week.
Kailin, here is another terrific blog post. I am really impressed with your technical work, and with your understanding of the process. Your inclusion of process and terminology demonstrates a deep level of involvement. Your diagrams are wonderful additions. Keep it up!
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