Research

Machine learning methods to analyze single-cell 3D genome data from mouse models of
psychiatric copy number variants

Paola Giusti-Rodriguez
Department of Psychiatry, College of Medicine

"Utilizing machine-learning bioinformatics methods, our
project seeks to ascertain the impact on the 3D-genome organization of mouse models of
human neuropsychiatric CNVs at a single-cell level to develop a mechanistic
understanding of neuropsychiatric CNVs and provide insight into their disease etiology."

Since January 2024, I have been a member of the Giusti-Rodriguez Lab. Here, I use bioinformatics tools to analyze and visualize multi-omic data on psychiatric disease. Starting in September 2024, I will begin using single-cell analysis pipelines to examine how interaction with disease-associated CNVs in the mouse genome results in different psychiatric phenotypes.

Aim 1: Generate single-cell chromatin conformation capture data from five mouse models of human neuropsychiatric CNVs using scSPRITE.
Aim 2: Ascertain the impact on the 3D-genome organization of mouse models of human neuropsychiatric CNVs at a single-cell level.
Aim 3: Integrate scSPRITE data with corresponding single-nuclei RNA sequencing data to unravel the relationship between chromatin organization and gene expression across five mouse models of neuropsychiatric CNVs.