FURI | Spring 2025

Analyzing Single-Cell RNA Sequencing Data to Classify Cell States

The Plaisier Lab developed and trained a cell cycle classifier (ccAF) on human neural stem cells and discovered a novel quiescent G0-like state while researching neuroepithelial-derived cell types in gliomas. Increasing the heterogeneity of tissue types used to train the cell cycle classifier (ccAF) and potentially identifying G0-like states in other tissues can extend the aforementioned benefits across a multitude of diseases found in the body. An optimized preprocessing pipeline will allow the researcher to determine which cell types can be accurately classified and suggest which cell types should be prioritized for experimental studies to expand the capabilities of the ccAF classifier to include a larger variety of biological contexts. This research will determine how well the ccAF classifier performs across a variety of biological contexts (cell types) from all three dermal layers.