Computational methods for delineating cell context-specific regulatory programs

标题：描述细胞环境特异性调控程序的计算方法

Title: Computational methods for delineating cell context-specific regulatory programs PI: Hatice Ulku Osmanbeyoglu, PhD Project Summary/Abstract Signaling-regulated transcription factors (TFs) orchestrate the developmental and differentiation trajectories of cells as well as their activation states. Understanding TF activities at the single-cell level represents a formidable challenge. Single-cell multi-omics technologies now measure different modalities such as RNA, surface proteins, and chromatin states. Moreover, emerging spatial technologies offer highly multiplex profiling of RNAs and proteins, while preserving spatial context of the tissue. Consequently, there is a tremendous need for computational methods that can integrate these measurements and infer the underlying cell type- and state- specific transcriptional programs. In response to this critical need, we developed SPaRTAN (Single-cell Proteomic and RNA based Transcription factor Activity Network) and integrated parallel single-cell proteomic, and transcriptomic data, based on Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) with cis-regulatory information (e.g. TF – target-gene priors) to predict cell-specific TF and surface protein activities. To the best of our knowledge, we are the first group to use CITE-seq data with cis-regulatory information for linking cell-surface receptors to TFs and construct cell-specific signaling linked regulatory programs. My research program develops interpretable machine learning approaches and computational tools to identify and characterize signaling-regulated TFs and spatial transcriptional heterogeneity for more concise understanding of cellular states. Here, we propose to advance our modeling efforts using context-specific chromatin accessibility data and simultaneously extend SPaRTAN to handle multiple cell-types and/or samples using multi-task and interpretable deep learning approaches based on single-cell multi-omics datasets (Goal 1). We will further develop computational methods for delineating spatially-informed cell context-specific transcriptional programs using spatial transcriptomics datasets (Goal 2). These methods will be integrated into software packages to make them widely accessible to the research community. We will exploit our methods to delineate cell context-specific TF activities that are both specific to humans and relevant to disease. Together, proposed frameworks have the potential to fill an important gap in knowledge by defining cell context-specific regulators driving cellular identity, as well as discover new targets and approaches for advancing therapy.