# Intracellular and Intercellular Network Rewiring and Hidden Driver Inference from Single-Cell Data

> **NIH NIH R01** · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · 2022 · $339,854

## Abstract

PROJECT SUMMARY
Biological processes operate through molecular networks at the cellular level, and through cell–cell networks at
the tissue/organ level. Deciphering the “wiring” and “rewiring” of these networks under healthy and pathological
conditions is a fundamental yet challenging goal of biomedical research. The emergence of single-cell RNA
sequencing (scRNA-seq) has presented an unprecedented opportunity to achieve this goal by enabling genome-
wide quantification of mRNA in thousands of cells simultaneously and overcoming the heterogeneity problem of
bulk omics data. However, deep analysis of scRNA-seq data is challenging because only a small fraction of the
transcriptome of each cell can be captured. No sophisticated computational tools are available to systemically
reverse engineer intracellular gene–gene (especially signaling) networks and intercellular cell–cell interaction
networks from single-cell omics data. Signaling proteins and epigenetic factors are crucial drivers of network
rewiring and are most likely druggable, making them ideal therapeutic targets. Unfortunately, it is often difficult
to unbiasedly identify many of these drivers (hence known as hidden drivers) because they may not be
genetically altered or differentially expressed at the mRNA or protein levels, but rather are altered by
posttranslational or other modifications. We have developed systems biology algorithms to expose hidden drivers
from bulk omics data for antitumor immunity, tumorigenesis, and drug resistance. However, it remains even more
challenging to reveal cell type–specific hidden drivers from scRNA-seq data because of the “dropout” effects.
Using our established state-of-the-art scRNA-seq platform, we profiled >100,000 epithelial cells from mouse
mammary gland. Our ultradeep scRNA-seq profiling identified new subsets of somatic mammary stem cells
(MaSCs) and shed light on the long-standing debate over the identities of multipotent and unipotent MaSCs.
Therefore, building upon our expertise in systems biology, our robust preliminary results, and our established
collaborations with leaders in the fields of breast cancer and immunology, we propose to develop computational
algorithms to reverse engineer intracellular gene-wise and intercellular cell-wise networks (Aim 1), determine
cell type–specific hidden drivers and their network rewiring (Aim 2), from single-cell omics data, and translate
findings toward biomarkers and therapeutics to improve patient care (Aim 3). We will use information theory and
Bayesian modeling in the development of these algorithms. We will use MaSCs and our breast cancer models
as a proof of concept. With the increasing affordability of single-cell omics technologies, our algorithms can have
a significant impact on many fields of biomedical investigation. For example, delineation of network rewiring and
of critical drivers in stem cells and their niches will provide vital insights into cancer metastasis and relapse, and
lay the...

## Key facts

- **NIH application ID:** 10476394
- **Project number:** 5R01GM134382-04
- **Recipient organization:** ST. JUDE CHILDREN'S RESEARCH HOSPITAL
- **Principal Investigator:** Jiyang Yu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $339,854
- **Award type:** 5
- **Project period:** 2019-09-09 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10476394

## Citation

> US National Institutes of Health, RePORTER application 10476394, Intracellular and Intercellular Network Rewiring and Hidden Driver Inference from Single-Cell Data (5R01GM134382-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10476394. Licensed CC0.

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