Computational methods for delineating cell context-specific regulatory programs

标题：描绘细胞环境特异性调控程序的计算方法 PI：Hatice Ulku Osmanbeyoglu，博士 项目概要/摘要 信号调节转录因子（TF）协调了细胞的发育和分化轨迹， 细胞及其激活状态。在单细胞水平上理解TF活动代表了一个强大的 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重的RNA分析， 蛋白质，同时保留组织的空间背景。因此，有一个巨大的需求， 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。为了满足这一关键需求，我们开发了SPaRTAN（单细胞 蛋白质组学和基于RNA的转录因子活性网络）和集成的并行单细胞蛋白质组学， 和转录组数据，基于通过测序的转录组和表位的细胞索引（CITE-seq） 顺式调节信息（例如TF -靶基因先验）预测细胞特异性TF和表面蛋白 活动据我们所知，我们是第一个使用CITE-seq数据与顺式调控基因的研究小组。 用于将细胞表面受体连接到TF的信息，并构建细胞特异性信号传导连接的调节 程序.我的研究项目开发可解释的机器学习方法和计算工具 识别和表征信号调节的TF和空间转录异质性， 了解细胞状态。在这里，我们建议使用特定于上下文的 染色质可及性数据，同时扩展SPaRTAN以处理多种细胞类型和/或样品 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）。 我们将进一步开发计算方法，用于描绘空间信息细胞上下文特异性 使用空间转录组学数据集的转录程序（目标2）。这些方法将被整合到 软件包，使他们广泛访问的研究界。我们将利用我们的方法， 描述细胞环境特异性TF活性，其对人类特异性且与疾病相关。在一起， 所提出的框架有可能通过定义特定于单元上下文的 调节驱动细胞身份，以及发现新的目标和方法，以推进治疗。

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.