"My experience as a GRC intern"

By Linh Le,MS; PhD Candidate in the Neuroscience Program

My internship experience with the Genomics Research Center (GRC) directly stemmed from my research interest in the role of microglia in Alzheimer’s disease (AD). Hence, it is only fair if I give a very brief introduction on the research field, before getting to why I chose this internship. Alzheimer’s disease (AD) is the most common age-related dementia, accounting for the progressive cognitive impairment and compromised life quality of millions of people worldwide. In AD pathophysiology, plaque and tangle accumulation trigger an inflammatory response that mounts positive feed-back loops between inflammation and protein aggregation, aggravating neurite damage and neuronal death. Microglia, the resident immune cells of the central nervous system, play a key role in AD pathogenesis. However, microglia exist in a spectrum of possible functional states; and which microglia states are helpful, harmful, or both and at what stage of disease progression; remains the topic of intense debate. The current advance in single cell sequencing techniques has truly begun to shed light on these difficult questions.

With permission from my mentor, I started working as a part-time intern with the GRC from September 2022 for a duration of three months. My main goal was to learn and perform downstream analysis of RNAseq and single-cell RNAseq data. Working with my direct supervisor, Dalia Ghoneim, Ph.D.,who is the Lead Bioinformatics Scientist and Ph.D. graduate from the University of Rochester, School of Medicine & Dentistry, Biomedical Genetics Department; we drafted weekly plan to tackle different topics. I started off with reading about statistical methods used in differential expression, enrichment analysis, single-cell data processing, as well as the R packages for those tasks. But moving on to analyze an experimental data set was a completely different experience than running mock dataset. I was overwhelmed by how many different ways the results can look depending on how the dataset were corrected, how many clusters to set on, or what markers to focus on. The most important lesson I learnt is that unlike bulk RNA-seq, where the analysis pipeline is very much standardized, single-cell RNA seq analysis requires a lot of arbitrary decisions based on the research questions and personal experience. Throughout, Dalia was extremely helpful and attentive to my progress. Furthermore, I also received help from other members of the bioinformatics team at the GRC. They kindly set up a Slack Channel for questions, which I gladly utilized. There are much more to learn, of course. I have only started to get the hang of things towards the end of the internship; and I would love to get exposure to the pre-processing steps.

I’m grateful for the unique opportunity I had at the GRC. My experience with RNA-sequencing analysis, albeit from the short and part-time internship, has proven to be a valuable asset not only for my own research but also to the lab. Since then, I have collaborated with two other lab members on sequencing projects, resulting in future co-authorship on publications. Looking ahead, I am certain that the experience I had will be beneficial in my future endeavor as I stay in the emerging field of neuro-immune interaction.

Katherine Bognanno | 3/14/2023

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