Computational methods for delineating cell context-specific regulatory programs

标题：用于描述特定于单元格的调节程序的计算方法 Pi：Hatice Ulku Osmanbeoglu，博士 项目摘要/摘要 信号调节的转录因子(TF)协调细胞的发育和分化轨迹 细胞及其激活状态。理解单细胞水平上的转铁蛋白活性是一项艰巨的任务 挑战。单细胞多组学技术现在测量不同的模式，如RNA，表面蛋白， 和染色质状态。此外，新兴的空间技术提供了高度多元化的RNA和 蛋白质，同时保持组织的空间背景。因此，存在着巨大的需求 可以整合这些测量并推断潜在细胞类型和状态的计算方法 特定的转录程序。为了满足这一迫切需求，我们开发了Spartan(单细胞 蛋白质组和基于RNA的转录因子活性网络)和整合的平行单细胞蛋白质组， 和转录组数据，基于转录组和表位的细胞排序(CITE-SEQ) 利用顺式调控信息(例如，转铁蛋白-靶基因先验)预测细胞特异性转铁蛋白和表面蛋白 活动。据我们所知，我们是第一个将CITE-SEQ数据与顺法规结合使用的组织 细胞表面受体与转录因子连接的信息及构建细胞特异性信号转导调控系统 程序。我的研究项目开发了可解释的机器学习方法和计算工具 为了更简洁地识别和表征信号调节的转录因子和空间转录异质性 对细胞状态的理解。在这里，我们建议使用特定于上下文的方法来推进建模工作 染色质可访问性数据，并同时扩展Spartan以处理多种细胞类型和/或样本 使用基于单细胞多组学数据集的多任务和可解释的深度学习方法(目标1)。 我们将进一步开发用于描述特定于空间信息的单元格的计算方法 使用空间转录学数据集的转录程序(目标2)。这些方法将整合到 软件包，以使研究社区能够广泛地访问它们。我们将利用我们的方法来 描述与细胞环境相关的转铁蛋白活动，这些活动既是人类特有的，也是与疾病相关的。一起， 拟议的框架有可能通过定义特定于单元格的上下文来填补知识中的一个重要空白 推动细胞识别的调节器，以及发现新的靶点和推进治疗的方法。

Title: Computational methods for delineating cell context-specific regulatory programs PI: Hatice Ulku Osmanbeyoglu, PhD 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.