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

细胞内和细胞间网络重新布线以及来自单细胞数据的隐藏驱动程序推断

基本信息

批准号：
10260637
负责人：
Jiyang Yu
金额：
$ 33.99万
依托单位：
ST. JUDE CHILDREN'S RESEARCH HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-09 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10260637
关键词：
Algorithms Bayesian Analysis Bayesian Method Bayesian Modeling Biological Assay Biological Markers Biological Process Biomedical Research Breast Cancer Model Cancer Relapse Cell Communication Cells Clinical Clinical Trials Clustered Regularly Interspaced Short Palindromic Repeats Collaborations Companions Computational Biology Computational algorithm Data Development Diagnosis Disease Dropout Drug resistance Engineering Epigenetic Process Epithelial Cells Exposure to Foundations Genes Genomics Goals Heterogeneity Homeostasis Immunologics Immunology Immunotherapy In Vitro Information Theory Investigation Laboratories Light Mammary gland Maps Mediating Messenger RNA Metabolic Modeling Modification Molecular Mus Nature Neoplasm Metastasis Network-based Organ Outcome Paper Pathologic Patient Care Patients Physicians Prevention Prognosis Proteins Proteomics Publications Resistance Saint Jude Children&apos s Research Hospital Signal Transduction Signaling Protein Systems Biology Technology Therapeutic Tissues Translating Translations Tumor Immunity algorithm development base cancer subtypes cancer therapy cell type computational pipelines computer framework computerized tools differential expression drug sensitivity functional genomics genome-wide human disease improved in silico in vitro Assay in vivo insight knowledge base malignant breast neoplasm mammary novel patient stratification precision oncology single-cell RNA sequencing stem cells targeted treatment therapeutic target transcriptome treatment strategy tumor tumor immunology tumorigenesis

项目摘要

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 foundation for understanding and overcoming the resistance of tumors to immunotherapies. Network- inferred hidden drivers are potential nonmutant therapeutic targets, and network-based biomarkers have tremendous potential to better stratify patients for precision cancer medicine.

项目总结