Utilizing Imaging Mass Cytometry to Identify Glioblastoma Cell Clusters (DSI-SRP)
This DSI-SRP fellowship funded Rohit Khurana to work in the laboratory of Dr. Rebecca Ihrie in the Department of Cell and Developmental Biology during the summer of 2021. Rohit is a junior with majors in Computer Science and Molecular & Cellular Biology.
The project funded by this fellowship aimed to understand how specific subpopulations of cells in glioblastoma – recently identified as predictive of patient outcome – organize themselves within specific geographical subregions. For example, do such cells congregate near the brain’s stem cell niche or around blood vessels? The proposed project sought to employ imaging mass cytometry (IMC), in which patient tissue sections were labeled using a high-dimensional panel of markers known to be differentially expressed across glioblastoma negative prognostic (GNP) and glioblastoma positive prognostic (GPP) cells. The resulting dataset – multichannel images where each channel corresponds to an experimental marker – would be subjected to a suite of data analysis techniques, including single-cell segmentation, feature extraction, and clustering algorithms.
This summer, Rohit’s project involved extensively utilizing immunohistochemistry (IHC) techniques to devise a validated experimental toolkit for detecting all markers of interest. A standardized sample block with multiple known controls was developed, which will be used to ensure results from multiple imaging runs are free from batch artefacts. Rohit will continue this project in the fall, where he hopes to continue to leverage data science methods to uncover hidden structure in his data and identify spatial distributions that suggest functional relationships between aggressive glioblastoma cells and specific brain structures.
In addition to receiving support through a DSI-SRP fellowship, this project was supported and facilitated by the DSI Data Science Team through their regular summer workshops and demo sessions.