Defense: Integrative Gene Regulatory Inference From single-cell data

Rojin Safari
Biomolecular Engineering & Bioinformatics PhD Candidate
Location
Virtual Event
Advisor
Josh Stuart

Join us on Zoom: https://ucsc.zoom.us/j/92135119235?pwd=cDdQMmxCbUtjY1E1Mys5NVlPcVA3UT09 / Passcode: 559493

Description: The human heart is one of the first organs to form during embryogenesis. By week 3, heart is a linear tube; then, between week 3 and 8, it undergoes a complex looping process to form heart chambers. From week 8 up until birth, the heart undergoes a period of rapid growth and expansion as the various cardiovascular lineages mature to support the developing fetus. Defects in this process lead to congenital heart defects. Therefore, understanding heart development at the molecular level will give insight into the mechanisms leading to the disease, and principles can further be applied for regenerative medicine purposes. This thesis investigates computational and statistical approaches that help understand the influences of molecular, genetic, and lifestyle components in maintaining a healthy heart.

Single-cell sequencing has provided a unique opportunity to study the heart at cellular resolution. Recent advances in single-cell sequencing allow scientists to sequence different modalities, such as proteomics, spatial transcriptomics, methylation, and chromatin accessibility at the level of individual cells. These advances offer a unique opportunity to develop computational methods for integrative single-cell data analysis to benefit from the information in multiple modalities.

Here I present Single-cell Integrative Gene Regulatory Network (sci-GRN) inference framework that uses single-cell transcriptomics and epigenomics to infer gene regulatory networks (GRNs), and I demonstrate its application to the human heart development. Here, I first describe how each omic is processed independently to cluster the cells. Next, I describe how we annotated cell types from scRNA-seq data using known marker genes. Third, I present how I used the scRNA-seq data to infer the expression and cell type of each ATAC cell. Lastly, I present how sci-GRN uses the integrated data to build a gene regulatory network.