Nandini Seetharaman ’22

Massachusetts General Hospital, Center of Genomic Medicine, Boston, MA

I worked in Professor Vijaya Ramesh’s lab at Massachusetts General Hospital (MGH) for nine weeks this summer. Professor Ramesh is a faculty member in the Center for Genomic Medicine, and her lab specializes in researching tuberous sclerosis complex (TSC), a genetic disorder characterized by benign tumor growth on various organs of the body such as the brain, kidneys, and skin.

When I originally received this internship opportunity, it was to work in the lab as a research assistant; however, due to Covid-19 restrictions, MGH labs were closed to non-employees. Fortunately, Professor Ramesh allowed me to stay on as a remote intern. My main project was to analyze data collected in the lab, and narrow down their findings to the results that looked most promising for future projects. We had weekly Zoom meetings where I presented the work I had done, and received advice or questions to look into. I did all my analysis work in R and Excel.

Before I joined, the Ramesh Lab had generated three cell lines from patients with TSC. One cell line was from a patient who was heterozygous for TSC; one cell line was from a patient who was homozygous null for TSC; the third cell line was a clone of the homozygous null patient cell line. The lab then collected transcriptome data on each cell line. Transcriptome data basically shows the change in gene expression between each cell line and a wildtype (a.k.a. a control/no TSC). At the same time, the lab received a large data set from NCATS (National Center for Advancing Translational Sciences) at the NIH that contained over 20,000 drugs and the genes they targeted. My main project was to find the overlap between these datasets. Essentially, I needed to find genes that: 1) were targeted by a drug and 2) seemed most correlated to the TSC condition. By the end of the project, I found 17 genes of note, with a corresponding list of around 50 drugs that looked promising for interfering with TSC signaling pathways.

This work experience has given me the opportunity to learn more about the kinds of data analytics that happen in a science research lab. Although this data project was only a result of circumstances not allowing me to come into the lab in person, I am actually grateful that I had this opportunity to learn more about analyzing lab data. I believe that I can apply this knowledge to my future projects at Williams and perhaps beyond in research jobs after I graduate. I’m also really happy that I got to work more with R. This was my first time using the software outside of a statistics class, and I’ve become a lot more proficient.

I am incredibly grateful to the Kraft Family for this summer opportunity; and I am also thankful to the ’68 Center for Career Exploration, who has always given me a lot of support and advice in shaping my time at Williams and beyond.