Studying temporal dynamics and regulatory mechanisms of single cells with a unified framework and multi-omics data

利用统一的框架和多组学数据研究单细胞的时间动态和调控机制

• 批准号: 10466944
• 负责人: Xiuwei Zhang
• 金额: $ 34.67万
• 依托单位: GEORGIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10466944
• 关键词: Biological; Cell Communication; Cell physiology; Cells; Chromatin; Data; Dependence; Dimensions; Formulation; Future; Gene Expression; Genomics; Individual; Knowledge; Learning; Location; Methods; Modality; Modeling; Multiomic Data; Organism; Output; Population; Research Personnel; Resolution; Signal Transduction; Technology; Time; cell type; data integration; design; gene regulatory network; multimodal data; multiple data types; multiple omics; single-cell RNA sequencing; technology development; transcription factor; transcriptome sequencing; transcriptomics

Single cell genomics technologies have allowed researchers to study differences between single cells. In order to understand how every cell functions in the whole living organism, cells need to be studied in the context of both time and space. Researchers would like to learn a comprehensive picture of each single cell, including its current cell state and predicted future state, and how it interacts with neighboring cells during the temporal dynamics. So the step after gaining a certain amount of knowledge of single cells is to go from “parts” to “whole”. This proposal discusses advances that can be brought to the study of both temporal dynamics and spatial interactions between cells. The theme of this proposal is “integration”. Existing integrative methods for single cell data focus on two scenarios: 1) integrate the same type of data (eg. RNA-Seq data) from multiple batches; 2) integrate multiple types of data performed on the same cells, which is also called multi-modality data or multi-omics data. This proposal highlights concepts and methods to integrate multi-omics data to understand cell temporal dynamics and regulatory mechanisms, while taking into account dependency between data modalities, which is rare in current methods. A new concept of “problem integration”, where related computational formulations can be connected to provide a consistent and more general picture of a certain aspect of cells, is also presented here. In order to study different aspects of a cell, different computational problems have been formulated, eg., clustering of cells, inference of cell trajectories, inference of gene regulatory networks (GRNs), etc. The idea of unifying or connecting related computational problems, such that a unified framework can involve or output the information that is previously used in multiple individual computational problems, is proposed. In particular, a unified framework for cell temporal dynamics analysis involving related computational tasks, is presented. So far the multi-omics integration methods often deploy the integrated data to cluster cells for cell type identification. Few methods on data integration are designed for temporal analysis with continuous populations, or to learn biological mechanisms like GRNs. So another direction proposed here is to infer the trajectory of cells with both gene-expression (scRNA-seq) and chromatin accessibility (scATAC-seq) data; with the inferred trajectory, the effect of chromatin accessibility on gene-expression can be studied, and GRNs can be reconstructed while taking into account this effect. In reality, a gene’s expression level is determined by multiple factors: its transcription factor (TF), its chromatin accessibility and the signal a cell receives from other neighboring cells through cell-cell interaction. Therefore, another important dimension to consider about the cells is the spatial location of cells. It is proposed to ßreconstruct a generalized GRN which models inter-cell regulatory interactions.

单细胞基因组学技术使研究人员能够研究单细胞之间的差异。为了 为了了解每个细胞在整个生物体中的功能，需要在以下背景下研究细胞： 时间和空间。研究人员希望了解每个单细胞的全面情况，包括其 当前细胞状态和预测的未来状态，以及它在时间过程中如何与相邻细胞相互作用。 动力学因此，在获得了一定数量的单细胞知识之后， “完整”这一建议讨论了可以带来的时间动力学研究的进展， 以及细胞间的空间相互作用。这份提案的主题是“融合”。 现有的单细胞数据整合方法主要集中在两种情况：1）整合相同类型的数据（例如， RNA-Seq数据）; 2）整合在相同细胞上进行的多种类型的数据，这是 也称为多模态数据或多组学数据。该提案强调了一些概念和方法， 整合多组学数据以了解细胞时间动态和调控机制， 考虑到数据模态之间的依赖性，这在当前方法中是罕见的。一个新 “问题集成”的概念，其中相关的计算公式可以连接到 提供了一个一致的和更全面的图片的某一方面的细胞，也提出了这里。 为了研究细胞的不同方面，已经制定了不同的计算问题，例如， 细胞的聚类、细胞轨迹的推断、基因调控网络（GRNs）的推断等。 统一或连接相关的计算问题，这样一个统一的框架可以涉及或 输出先前在多个单独的计算问题中使用的信息。在 特别是，涉及相关计算的细胞时间动力学分析的统一框架， 任务，提出。 到目前为止，多组学集成方法通常将集成的数据部署到细胞类型的细胞聚类中 识别.很少有数据集成方法是为连续总体的时态分析设计的， 或学习生物机制如GRNs。所以这里提出的另一个方向是推断 具有基因表达（scRNA-seq）和染色质可及性（scATAC-seq）数据的细胞; 轨迹，可以研究染色质可及性对基因表达的影响，并且可以将GRNs 在考虑到这一影响的情况下进行重建。 事实上，基因的表达水平是由多种因素决定的：其转录因子（TF），其染色质 可访问性和小区通过小区-小区交互从其它相邻小区接收的信号。因此，我们认为， 关于细胞要考虑的另一个重要维度是细胞的空间位置。提出要 <$重建一个广义GRN模型细胞间的调节相互作用。