Advisor: Ian Tobias [1], Biomedical Sciences

Proposed biological advisor: Jasmin Lalonde [2], Terry Van Raay [3]

This project involves the analysis single-cell RNA-sequencing (scRNA-seq) data from embryonic mouse brain samples to investigate the heterogeneity of neural progenitor cells (NPCs) and their developmental programming. The study will utilize existing scRNA-seq pipelines for initial data processing and quality control, followed by custom downstream analyses to address specific research questions. The initial study objectives are: 

1. To apply dimensionality reduction techniques to identify distinct subpopulations of NPCs in the embryonic brain.
2. To use trajectory inference to predict the developmental dynamics of mouse NPCs 
3. To develop visualizations based on differential expression data and existing knowledge that identifies transcriptional regulators predicted to regulate these fate decisions

This project provides an excellent opportunity for a bioinformatics student to gain hands-on experience in scRNA-seq data analysis, high performance computing, reproducible programming, and the development visualizations of high-dimensional data, while contributing to our understanding of fundamental neurodevelopmental processes.

This project is suitable for one or two semesters. The student is required to be on-site.

Knowledge/Skills

Basic understanding of central nervous system anatomy, Fundamental knowledge of genetics and biochemistry, Intermediate software carpentry skills (Bash, Python, and R), Familiarity with the relevant statistical models (General Linear Models, Dimensionality Reduction methods), Any experience with containerized pipelines is a plus.