Methods for inferring and analyzing gene regulatory networks using single-cell multiomics and spatial genomics data

项目摘要/摘要 基因调控的研究为破译COM的发展提供了基础的机制知识。 Plex生物系统，并通过操纵细胞状态设计疾病治疗策略。发展 单细胞、多模式和多组学测序技术的出现允许对额外的表观基因组学进行fi 信息，如顺式调控元件，使基因调控的推断超越简单的基因 共表达。然而，为了更准确地识别基因调控，迫切需要计算方法。 将在同一细胞中测量的转录组和表观基因组学数据合并在一起。最近的研究表明， 表观基因组学的变化可能预示着基因转录，表明基因调控是一种不同步的 进程。一组调节子可能不会在整个连续的生物过程中fl靶基因，但 只在一个很小的时间窗口内。然而，从时间序列数据推断的当前基因调控网络提供了 一段时间内的平均监管，而不是描述监管动态。此外，先前的证据表明 细胞间的交流和相互作用存在于空间定位的细胞群体中，作为一种规则的- 规模化的活动。进一步将随时间变化的发育轨迹纳入组织空间景观将有助于 对基因调控的空间异质性进行分类。然而，我们仍然缺乏计算方法来增加- 基因调控推断中的时空轨迹。为了缩小这些差距，我的研究计划将 (1)发展计算方法和统计方法来推断静态和动态的基因调控 网络，(2)利用从多组学和空间转录数据推断的空间轨迹来特征- 空间基因调控，以及(3)开发基因调控网络的控制策略以达到预期的效果 国家使用多样本单细胞多组学数据。我的长期目标是描述基因调控的特征 多个样本和空间分离的组织区域，并确定操纵的最小驱动基因集- 更新小区状态(例如，小区类型)。根据我以前在基因调控网络推断和研究方面的经验 方法发展分析多样本单细胞基因组学和表观基因组学数据，我将开发新的 利用多样本单细胞多组学数据推断基因调控网络的统计方法。 改变时间滞后的活动。我将在我最近关于单细胞伪时间分析和单细胞的工作的基础上继续工作 在时间和时空背景下推断基因调控的时空轨迹。更进一步，建设 关于网络控制的理论研究和多样品单细胞分析方法的发展 组学数据，我将为基因调控网络开发控制策略，以实现所需的细胞状态或细胞去中心化 具有最小一组驱动基因的发育轨迹。最后，我将对这些方法进行系统的评价 使用可公开获得的数据跨越组织和样本条件。所有方法将普遍适用于 不同的组织或疾病背景。这些方法将以公开和免费的方式发布给社区 可用的开源软件，可以惠及广大的科学fific社区。

Project Summary/Abstract The study of gene regulation provides fundamental mechanistic knowledge to decipher the development of com- plex biological systems and design treatment strategies for diseases by manipulating cell states. Development of single-cell multimodal and multiomics sequencing technologies allows for profiling of additional epigenomics information, such as cis-regulatory elements, which enables inference of gene regulation beyond simply gene coexpression. However, to more accurately identify gene regulation, computational methods are urgently needed to incorporate both transcriptomics and epigenomics data measured in the same cell. Recent studies show that epigenomics changes may foreshadow gene transcription, indicating that gene regulation is an asynchronous process. A set of regulators may not influence the target gene along the whole continuous biological process but only in a small time window. However, current gene regulatory networks inferred from time series data provide average regulation across time rather than characterizing regulation dynamics. Also, prior evidence shows that cell–cell communication and interaction are present in spatially localized cell populations as a form of regular- ized activity. Further incorporating time-varying developmental trajectories in tissue-spatial landscapes will help categorize the spatial heterogeneity of gene regulation. However, we still lack computational methods to incor- porate spatio-temporal trajectories in gene regulation inference. To close these gaps, my research program will (1) develop computational methods and statistical approaches to infer both static and dynamic gene regulatory networks, (2) leverage the spatial trajectories inferred from multiomics and spatial transcriptomics data to charac- terize spatial gene regulation, and (3) develop control strategies for gene regulatory networks to achieve desired states using multi-sample single-cell multiomics data. My long-term goal is to characterize gene regulation across multiple samples and in spatially separated tissue regions and identify the minimum set of driver genes to manip- ulate cell states (e.g., cell types). Building on my previous experience in gene regulatory network inference and methods development to analyze multi-sample single-cell genomics and epigenomics data, I will develop novel statistical methods to infer gene regulatory networks using multi-sample single-cell multiomics data and char- acterize time-lagged activities. I will build on my recent work on single-cell pseudotime analysis and single-cell spatio-temporal trajectories to infer gene regulation in temporal and spatio-temporal contexts. Further, building on my theoretical research in network control and methods development in analyzing multi-sample single-cell omics data, I will develop control strategies for gene regulatory networks to achieve desired cell states or cell de- velopmental trajectories with a minimum set of driver genes. Finally, I will systematically evaluate these methods across tissues and sample conditions using publicly available data. All methods will be generally applicable in different tissue or disease contexts. These methods will be released to the community with publicly and freely available open-source software that can benefit the broad scientific community.