Inferring Gene Regulatory Networks Governing Definitive Endoderm Differentiation from Single Cell RNA Velocity Measurements

从单细胞 RNA 速度测量推断控制定形内胚层分化的基因调控网络

基本信息

批准号：
10618963
负责人：
Jolene Sarah Ranek
金额：
$ 0.63万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-02 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10618963
关键词：
Air Movements Algorithms Automobile Driving Back Breathing CRISPR interference Cell Lineage Cell Therapy Cell physiology Cells Chronic Obstructive Pulmonary Disease Computing Methodologies Consensus Coupling Data Decision Making Development Differential Equation Disease Disease model Encapsulated Endoderm Endoderm Cell Event Fellowship Fibroblast Growth Factor Foundations Future Gene Expression Gene Expression Profile Gene Expression Profiling Genes Genetic Transcription Goals Heterogeneity Individual Knowledge Lung Lung diseases Measurement Metabolism Methods Modeling Molecular Morphogenesis Natural regeneration Pathway interactions Persons Pluripotent Stem Cells Population Prevalence Process Proliferating Protocols documentation Quality of life RNA RNA Splicing Regulator Genes Research Role SOX17 gene Specific qualifier value Structure of parenchyma of lung Testing Time Tissue-Specific Gene Expression Training Transcript Transforming Growth Factor beta Undifferentiated Variant World Health Organization cell fate specification cell type computational pipelines computer studies differentiation protocol directed differentiation experimental study gene interaction gene network gene regulatory network human embryonic stem cell human pluripotent stem cell improved insight lung development lung injury lung regeneration mathematical model models and simulation novel pluripotency precursor cell regenerative self-renewal single-cell RNA sequencing skills small molecule stem cell biology stem cells temporal measurement transcription factor transdifferentiation

项目摘要

Project Summary The World Health Organization estimates that over 65 million people suffer from moderate to severe chronic obstructive pulmonary disease, a condition characterized by poor airflow and restricted breathing 1. The ability to regenerate damaged lung tissue would dramatically improve the quality of life for these individuals while reducing the prevalence and burden of pulmonary diseases worldwide. A promising approach to this problem is to use human pluripotent stem cells to produce lung and airway progenitor cells. Indeed, specialized protocols have been developed to convert stem cells into definitive endoderm, a lung precursor cell type 10-19. These protocols use small molecules to modulate the expression of key regulators of lung development including WNT, TGFβ, BMP, and FGF; however, these protocols are limited by the inability to generate a homogeneous population of definitive endoderm cells 11,15. This problem necessitates a better mechanistic understanding of how individual cells transition from their pluripotent cell state into definitive endoderm. Specifically, there is a critical need to understand how the gene regulatory networks in a given cell control its morphogenesis, proliferation, and differentiation decisions. Therefore, with the long-term goal of increasing homogeneity in lung precursor cells, the research objective of this fellowship is to determine how transcriptional heterogeneity in human embryonic stem cells influences their commitment to definitive endoderm. I hypothesize that heterogeneity in the starting population of cells generates alternate trajectories to definitive endoderm (or other cell types) and that these differences increase over time due to mutual inhibition between specific pairs of transcription factors (e.g., OCT4/SOX17, NANOG/GATA6). To test this hypothesis, I will first use single-cell RNA sequencing26 to define the transcriptional heterogeneity in human pluripotent stem cells during differentiation to definitive endoderm. I will then quantify the time-dependent changes in gene expression for each cell using RNA velocity, a computational method that uses spliced and unspliced transcript counts to estimate future gene expression states 28-29. Using these single-cell measurements, I will then develop a mechanistic model of the gene regulatory networks governing differentiation to DE and validate the model using known gene-gene interactions. Model simulations will: (1) confirm major gene regulators that drive differentiation; (2) identify novel gene networks that control heterogeneity before and during differentiation; and (3) reveal crosstalk among gene regulatory networks governing differentiation and other ongoing cellular processes such as proliferation and metabolism. The proposed experimental and computational studies provide a general framework to systematically identify gene regulatory mechanisms controlling differentiation to definitive endoderm and aid in the development of more efficient and homogeneous differentiation/transdifferentiation protocols for regenerative cellular therapies.

项目概要世界卫生组织估计，超过 6500 万人患有中度至重度慢性病阻塞性肺病，一种以气流不畅和呼吸受限为特征的疾病 1. 能力再生受损的肺组织将极大地改善这些人的生活质量，同时减少全世界肺部疾病的患病率和负担是解决这一问题的有希望的方法。是利用人类多能干细胞来产生肺和气道祖细胞，确实是专门的。已开发出将干细胞转化为定形内胚层（肺前体细胞类型 10-19）的方案。这些方案使用小分子来调节肺部发育关键调节因子的表达包括 WNT、TGFβ、BMP 和 FGF；然而，这些协议由于无法生成定形内胚层细胞的同质群体11,15 这个问题需要更好的机制。了解单个细胞如何从多能细胞状态转变为定形内胚层。具体来说，迫切需要了解给定细胞中的基因调控网络如何控制其因此，长期目标是增加形态发生、增殖和分化。肺前体细胞的同质性，该奖学金的研究目标是确定如何人类胚胎干细胞的转录异质性影响其对最终决定性的承诺我追寻的是起始细胞群的异质性会产生不同的轨迹。定形内胚层（或其他细胞类型），并且由于相互抑制，这些差异随着时间的推移而增加特定转录因子对之间（例如 OCT4/SOX17、NANOG/GATA6）。将首先使用单细胞 RNA 测序26 来定义人类多能干细胞的转录异质性然后我将量化基因随时间的变化。使用RNA速度计算每个细胞的表达，这是一种使用剪接和未剪接转录本的计算方法使用这些单细胞测量值来估计未来的基因表达状态 28-29。开发控制分化为 DE 的基因调控网络的机制模型并验证使用已知的基因-基因相互作用的模型模拟将：（1）确认驱动的主要基因调节因子。分化；（2）识别分化前和分化过程中控制异质性的新基因网络； (3)揭示控制分化和其他正在进行的细胞的基因调控网络之间的串扰所提出的实验和计算研究等过程。提供一个通用框架来系统地识别控制分化的基因调控机制定形内胚层并有助于更有效和均质的发育再生细胞疗法的分化/转分化方案。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

DELVE: feature selection for preserving biological trajectories in single-cell data.

DELVE：在单细胞数据中保存生物轨迹的特征选择。

DOI：
发表时间：
2024-03-29
期刊：
影响因子：
16.6
作者：
Ranek, Jolene S;Stallaert, Wayne;Milner, J Justin;Redick, Margaret;Wolff, Samuel C;Beltran, Adriana S;Stanley, Natalie;Purvis, Jeremy E
通讯作者：
Purvis, Jeremy E

Feature selection for preserving biological trajectories in single-cell data.

在单细胞数据中保存生物轨迹的特征选择。