# Computational methods for delineating cell context-specific regulatory programs

> **NIH NIH R35** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $384,284

## Abstract

Computational methods for delineating cell context-specific regulatory programs
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.

## Key facts

- **NIH application ID:** 10500402
- **Project number:** 1R35GM146989-01
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Hatice Ulku Osmanbeyoglu
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $384,284
- **Award type:** 1
- **Project period:** 2022-09-08 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10500402

## Citation

> US National Institutes of Health, RePORTER application 10500402, Computational methods for delineating cell context-specific regulatory programs (1R35GM146989-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10500402. Licensed CC0.

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