Ph.D. Proposal: Honglin Wang

Major Advisor: Dr. Dong-Guk Shin
Advisory Committee:  Dr. Sheida Nabavi, Dr. Peter F. Maye
Review Committee:  Dr. Derek Aguiar, Dr. Ion Mandoiu
Date/Time: Tuesday, February 21st, 2023, 11:00 am
Location: WebEx

Meeting link: https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=mf18e5fed38a55a50097ec44d159fe326

Meeting number: 2620 515 9435

Password:

VxT2qaqnf94

Abstract

Cell-cell communication fundamentally regulate biological tissue states. Historically, studying cell-cell communication has been attempted for one or two cell types with a handful set of genes. With the emergence of single-cell transcriptomics, we are now able to study the genome-wide gene expression profiles at single cell level. The availability of single cell transcriptomics data presents exciting opportunities as well as challenges in constructing comprehensive tissue-specific cell-cell communication networks. There have been multiple methods developed to infer cell-cell communication. However, these methods have a common limitation—they do not take advantage of up/downstream gene regulatory activities in inferring cell-cell communication. Previously, we developed a route-based pathway analysis method, and we find this method readily applicable to inferring potential up/downstream regulatory activities pertaining to cell-cell communication. In this work, we discuss first how publish biological knowledge (e.g., pathway databases and gene interaction databases) can be extended by using newly acquired experimental data. Specifically, we present how a pathway route in a published pathway network can be extended by predicting the potential target genes for a transcription factor. Transcription targets are context specific and they are typically omitted in published pathway resources (e.g., KEGG).  We then discuss how pathway routes can be interlinked by using gene regulatory information obtainable from third party gene interaction databases (e.g., TURRST2). Lastly, we present a route-based cell type communication inference framework that is capable of suggesting how population-based cell-cell communication network can be extended by including its up/downstream regulatory responses. For future work, we propose to extend the framework to infer cell-cell communication at a single cell level by integrating single cell RNA-seq data with spatial transcriptomics data.