Leveraging multi-species single cell omic datasets to study the evolution of cell type-specific gene regulatory networks

利用多物种单细胞组学数据集研究细胞类型特异性基因调控网络的进化

基本信息

批准号：
10710055
负责人：
Sushmita Roy
金额：
$ 46.48万
依托单位：
UNIVERSITY OF WISCONSIN-MADISON
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-26 至 2026-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10710055
关键词：
ATAC-seq Acute Address Automobile Driving Awareness Biological Biological Assay Biology Brain CISH gene Cell Lineage Cell Nucleus Cells Chromatin Chromosome Mapping Clinical Clustered Regularly Interspaced Short Palindromic Repeats Comparative Study Computing Methodologies Data Data Analyses Data Set Development Disease Elements Evolution Excision Exhibits Family suidae Gene Expression Gene Expression Profile Gene Expression Regulation Genes Genomic Segment Growth Heterogeneity Human Individual Kidney Kidney Diseases Learning Measurement Measures Medicine Methods Modeling Molecular Mus Nephrectomy Organ Pathologic Processes Pathway Analysis Patients Pattern Phenotype Phylogenetic Analysis Phylogeny Physiology Play Population Primates Process Property Proteins Publishing Rattus Renal function Research Design Research Personnel Resolution Retina Rodent Role Sampling Small Interfering RNA Software Tools Source Specific qualifier value Specificity Structure System Technology Testing Time Tissues Transgenes Transposase Work cell fate specification cell type comparative computerized tools computing resources functional genomics gene conservation gene regulatory network genetic regulatory protein infancy innovation insight machine learning method multiple datasets multiple omics multitask novel programs single cell technology single-cell RNA sequencing spatiotemporal tool trait transcription factor trend validation studies

项目摘要

PROJECT SUMMARY Comparative functional genomics offers a powerful framework to study the molecular underpinnings of species- specific traits. Gene regulatory networks (GRNs) which control precise context-specific expression patterns of genes play a significant role in diversifying phenotypes across species. These networks are central to cell type specific function and are often disrupted in many diseases. However, comparison of gene regulatory networks across species has been challenging because of the lack of sufficient number of samples across matched biological contexts. Single cell omic technologies, such as single cell RNA-seq (scRNA-seq) and ATAC-seq (scATAC-seq), are revolutionizing biology enabling researchers to profile the activity of nearly all genomic regions in each individual cell. Single cell omic studies are quickly expanding to multiple species providing unprecedented opportunities to define cell types and their underlying gene regulatory networks and study their evolution. However, computational methods for defining cell-types and cell-specific GRNs across species are in their infancy. In particular, samples in a multi-species scRNA-seq dataset are related by a phylogeny, however, existing integration approaches do not model these relationships. Furthermore, existing approaches are restricted to one-to-one relationships across species, which makes it difficult to study some of the major sources of evolutionary innovation (e.g., duplications) in cell type identity. In this project, we will develop novel computational methods to tackle two problems: (a) defining cell types and their lineage relationships across species from scRNA-seq and scATAC-seq datasets, (b) inference and comparative analysis of cell type-specific GRNs across species from single cell RNA-seq and ATAC-seq data. Our tools will be based on machine learning methods, namely, probabilistic graphical models, multi-task and multi-view learning, and matrix factorization, that offer principled frameworks to integrate information across species. We will first test these tools in human and mouse scRNA-seq/ATAC-seq datasets from our collaborators and published studies. We will demonstrate the full potential of our tools on a novel multi-species kidney scRNA-seq/scATAC-seq dataset that we will collect to study normal kidney function as well as compensatory renal growth, which controls how one kidney recovers after surgical removal of another kidney. We will identify conserved and diverged regulatory networks that will be used to prioritize sequence and protein regulators for validation studies with CRISPR and siRNA. Our analysis will reveal key insights into how GRNs evolve across species and how they establish different cell types. Our approaches and novel datasets will provide critical insight into the molecular programs governing kidney structure and function that could have a significant clinical impact for patients with kidney disease. Our methods will constitute a suite of broadly applicable tools that can shed insight into principles of gene regulation and cell fate specification that will be applicable to single cell datasets from diverse multi-cellular systems.

项目概要比较功能基因组学为研究物种的分子基础提供了一个强大的框架—— 具体特征。基因调控网络（GRN）控制精确的上下文特定表达模式基因在跨物种表型多样化方面发挥着重要作用。这些网络是细胞类型的核心特定的功能在许多疾病中经常被破坏。然而，基因调控网络的比较跨物种一直具有挑战性，因为缺乏足够数量的匹配样本生物学背景。单细胞组学技术，例如单细胞 RNA-seq (scRNA-seq) 和 ATAC-seq （scATAC-seq）正在彻底改变生物学，使研究人员能够分析几乎所有基因组的活性每个单独细胞中的区域。单细胞组学研究正在迅速扩展到多个物种，提供定义细胞类型及其潜在基因调控网络并研究它们的前所未有的机会进化。然而，用于定义跨物种细胞类型和细胞特异性 GRN 的计算方法是在他们的婴儿期。特别是，多物种 scRNA-seq 数据集中的样本通过系统发育相关，但是，现有的集成方法没有对这些关系进行建模。此外，现有的方法是仅限于跨物种的一对一关系，这使得研究一些主要来源变得困难细胞类型识别中的进化创新（例如重复）。在这个项目中，我们将开发新颖的解决两个问题的计算方法：（a）定义细胞类型及其谱系关系来自 scRNA-seq 和 scATAC-seq 数据集的物种，(b) 细胞类型特异性的推断和比较分析来自单细胞 RNA-seq 和 ATAC-seq 数据的跨物种 GRN。我们的工具将基于机器学习方法，即概率图模型、多任务和多视图学习以及矩阵分解，提供原则框架来整合跨物种的信息。我们将首先在人类和来自我们合作者和已发表研究的小鼠 scRNA-seq/ATAC-seq 数据集。我们将展示我们的工具在新型多物种肾脏 scRNA-seq/scATAC-seq 数据集上的全部潜力，我们将收集该数据集研究正常肾功能以及代偿性肾脏生长，这控制着一个肾脏的恢复方式手术切除另一个肾脏后。我们将确定保守且分散的监管网络用于优先考虑序列和蛋白质调节因子，以进行 CRISPR 和 siRNA 的验证研究。我们的分析将揭示有关 GRN 如何跨物种进化以及它们如何建立不同细胞类型的关键见解。我们的方法和新颖的数据集将为控制肾脏的分子程序提供重要的见解结构和功能可能对肾病患者产生重大临床影响。我们的方法将构成一套广泛适用的工具，可以深入了解基因调控和细胞的原理命运规范将适用于来自不同多细胞系统的单细胞数据集。