Breanna McBean, a senior applied mathematics major at California State University, Fullerton, developed methods to analyze Slide-seq kidney data.
Diabetic kidney disease (DKD), or damage sustained by the kidneys as a result of diabetes, is one of the leading chronic kidney diseases worldwide, and its prevalence is projected to increase in the coming years as diabetes becomes more widespread.
Something truly special about the Broad Institute is the extraordinary network you can build here. Broad scientists both conduct groundbreaking research in their fields and eagerly offer their experience to support developing students, celebrating our successes and helping us move forward. From these relationships, I learned that there are many paths that lead to science, and it all starts with a passion for learning. DKD results in reduced kidney function, which can cause buildup of waste and fluid in the body. With continued damage, complete kidney failure can occur, necessitating dialysis and transplant. Due to the rise of DKD, and the severity of its effects, it is crucial to identify the mechanisms of kidney injury in order to be able to improve patient outcomes. To explore cellular changes associated with DKD, we used Slide-seq, a new technology that allows for the study of spatial transcriptomics of tissue at a near-single-cell resolution. We developed statistical methods to analyze kidney Slide-seq data by leveraging machine learning techniques. These methods seek to measure phenomena like the co-localized expression of multiple genes or the presence of a gradient of expression within a cell type using permutation-based testing. We used these methods to quantify spatial patterns in DKD kidneys, some of which we have observed in previous Hybridized Chain Reaction (HCR) experiments. The quantitative characterization of physiological changes caused by DKD will facilitate the discovery of new therapeutic targets for DKD.
Project: Developing a Method for Slide-seq Analysis of Kidney Tissue
Mentors: Jamie Marshall and Qingbo Wang, Medical and Population Genetics