This summer I had the honor of assisting in Dr. Chen’s lab, which is aiming to restore the function of injured brains. The main project that I worked on was analyzing the videos of rat dissociated neurons after in-vitro stimulation. I used a program called FluoroSNNAP, developed by the Meaney lab at Penn, to segment the videos or to highlight all the regions of interest in the video that should be considered as neurons. This process was sometimes quite tedious; however, it was a valuable experience, for it pointed out that although research can really seem glamorous, the process of obtaining meaningful insight can actually be boring at times but definitely necessary. In my case, without segmenting repeatedly, all the efforts that went into collecting videos of neuron activity would be in vain. After segmenting, I ran the segmented files through the code that a previous lab member already wrote in order to obtain data on the average number of neuron activities and information on their connectivity.
Additionally, I helped optimize the data analysis process. When segmented files were analyzed previously, the spike profiles of the selected regions of interests were compared to a library of spike profiles in the FluoroSNNAP app. However, since the neurons in the data that we were collecting can potentially be different from this standard library of profiles, we decided to create a new library for rat cells, human cells, and organoids. For each kind of cell/organoid, I selected a video that seems to have robust activity and used the activity of neurons in this video to build the new library of spike profiles. Since the video that was used to build the library cannot be analyzed with the library that it was used to create, I have selected additional videos that show strong activity, and the next step is comparing the results of analyzing videos using the new library and the old spike library.
My experience this summer was overall very rewarding. In the past, my lab experience was limited to bench work. Working with codes and large amounts of data has helped tremendously in improving my quantitative skills, and I am truly grateful for the opportunity.