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Measuring and modeling the dynamics of patterning in human stem cells

人类干细胞模式动态的测量和建模

基本信息

批准号：
10084170
负责人：
Sharad Ramanathan
金额：
$ 32.52万
依托单位：
HARVARD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-01-11 至 2022-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10084170
关键词：
Address Affect Amino Acid Motifs Animals Anterior Apical Awareness BMP4 Cell Differentiation process Cells Complex Congenital Abnormality Data Data Analyses Defect Development Developmental Biology Disease Embryo Endoderm Epiblast Epithelial Exposure to Family Future Gene Expression Genes Genetic Transcription Genome Geometry Germ Layers Goals Hour Human Human Development Image In Vitro Individual Ligands Literature Location Malignant Neoplasms Mathematics Measures Mesoderm Methods Microfluidic Microchips Microfluidics Microscopy Modeling Modification Molecular Molecular Biology Mus Pattern Positioning Attribute Primitive Streaks Process Publishing Regulator Genes Signal Transduction Smad Proteins Statistical Methods Statistical Models System Techniques Testing Time Tissues Transforming Growth Factor beta Transforming Growth Factor beta Receptors Work apical membrane base basolateral membrane blastomere structure bone morphogenetic protein receptors cell type course development developmental disease digital endodermal progenitor experimental study germline stem cells human RNA sequencing human embryonic stem cell human embryonic stem cell line human stem cells human tissue image processing in vivo knock-down mathematical model microscopic imaging mouse development multipotent cell mutant novel predicting response predictive modeling prevent progenitor receptor response single cell analysis single cell sequencing single-cell RNA sequencing stem cells tool transcription factor

项目摘要

Abstract The long-term goal of this project is to understand how cells in complex human tissues sense, process and respond to signal during normal human development and developmental diseases. A fundamental question in developmental biology is to understand how tissues are patterned in a developing animal. This Application will address the question in the context of the patterning of the human pluripotent cells into mesoderm and endoderm. The first question that will be investigated is how cells sense signal, followed by a closer look at how the internal gene regulatory network processes this signal to launch a transcriptional response as the cell chooses its fate. The first aim of this proposal uses a combination of novel microfluidics to control gradients of signals, genome modification techniques to fluorescently tag key transcription factors to study their dynamics using epifluorescence, and image processing and mathematical tools to analyze the data. It further follows up on the applicant’s recent discovery that key receptors that sense signals are basally localized. This study demonstrates how the (in)ability of the cell to sense active apical signal due its receptors being localized basally affect patterning. This is the first study the applicants are aware of to attempt to quantitatively understand how human stem cells are patterned. The second aim focuses on understanding how the state of the gene regulatory network within the cell affects the response to TGF-beta signal during germ layer differentiation in human and mouse. Indeed, cells even twelve hours apart in development obtained from the same region of the embryo show digitally distinct responses to the same signal. Single cell gene expression data obtained during the course of development is used to build a predictive mathematical model of the intracellular gene regulatory network. Building such predictive mathematical models has been very challenging in the past. Using these models, the goal of this aim is to uncover whether cells can respond to the same morphogenetic signal in distinct ways depending on the state of a core gene regulatory circuit. The predictions are checked experimentally in the context of early human and mouse development using imaging and molecular techniques to perturb gene expression. The discoveries made by the proposal will lead to a better understanding of how multipotent human cells respond to signal both during development and in cancer. Furthermore, the ability to build predictive models of the underlying gene regulatory network opens avenues to understand the mechanisms underlying disease states in the future.

摘要