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

项目摘要 生物过程在细胞水平上通过分子网络进行，在细胞水平上通过细胞-细胞网络进行。 组织/器官水平。在健康和病理状态下，破译这些网络的“布线”和“重新布线” 条件是生物医学研究的一个基本但具有挑战性的目标。单细胞RNA的出现 scRNA-seq技术为实现这一目标提供了前所未有的机会， 同时在数千个细胞中广泛定量mRNA，并克服了 批量组学数据。然而，scRNA-seq数据的深入分析是具有挑战性的，因为只有一小部分的scRNA-seq数据是不完整的。 可以捕获每个细胞的转录组。没有复杂的计算工具可以系统地 反向工程细胞内基因-基因（尤其是信号）网络和细胞间细胞-细胞相互作用 从单细胞组学数据构建网络。信号蛋白和表观遗传因子是网络的关键驱动因素 重新连接，并且很可能是药物，使它们成为理想的治疗目标。不幸的是， 无偏见地识别这些驱动程序中的许多（因此称为隐藏驱动程序），因为它们可能不是 在mRNA或蛋白质水平上发生遗传改变或差异表达，而是通过 翻译后或其它修饰。我们开发了系统生物学算法来揭露隐藏的驱动因素 从抗肿瘤免疫、肿瘤发生和耐药性的批量组学数据中。然而，它仍然更加 由于“脱落”效应，从scRNA-seq数据揭示细胞类型特异性隐藏驱动因素具有挑战性。 使用我们建立的最先进的scRNA-seq平台，我们分析了来自小鼠的> 100，000个上皮细胞。 乳腺我们的超深scRNA-seq分析鉴定了体乳腺干细胞的新亚群 本研究旨在阐明多能和单能MaSCs的特性，并阐明关于多能和单能MaSCs特性的长期争论。 因此，基于我们在系统生物学方面的专业知识，我们强大的初步结果，以及我们建立的 与乳腺癌和免疫学领域的领导者合作，我们建议开发计算 反向工程细胞内基因和细胞间细胞网络的算法（目标1），确定 细胞类型特定的隐藏驱动程序和它们的网络重新布线（目标2），从单细胞组学数据，并翻译 生物标志物和治疗方法的发现，以改善患者护理（目标3）。我们将使用信息理论， 贝叶斯建模在这些算法的发展。我们将使用MaSCs和乳腺癌模型 作为概念的证明。随着单细胞组学技术的日益普及，我们的算法可以 对生物医学研究的许多领域产生了重大影响。例如，划定网络重新布线和 干细胞及其利基的关键驱动因素将为癌症转移和复发提供重要见解， 为了解和克服肿瘤对免疫治疗的耐药性奠定基础。网络- 推断隐藏的驱动程序是潜在的非突变治疗靶点，基于网络的生物标志物具有 巨大的潜力，以更好地分层患者的精确癌症医学。

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.