Determining lineage decisions and gene regulatory networks governing the generation of key progenitor cell types during early human brain development
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Principal Investigator: Sharad Ramanathan Organization: HARVARD UNIVERSITY Fiscal Year: 2024 Award: $469,127 Funding agency: Eunice Kennedy Shriver National Institute of Child Health and Human Development Abstract The long term goal of this proposal is to quantitatively understand how gene regulatory networks (GRNs) generate the diversity of cell types during the development of the human brain. The focus of this proposal is to determine how key progenitor cell types that are uniquely enriched in humans are generated. Such an understanding is essential for uncovering the mechanisms of human developmental diseases. There are three challenges to achieving this goal: 1. Ethical issues in working with developing human tissue, 2. Computational and experimental techniques to determine the sequence of progenitor cell states and state transitions that give rise to the diversity of cell types, 3. the difficultly in building quantitative models of the gene regulatory networks in the absence of data to determine the thousands of biochemical constants. The approach of the proposal is to build the necessary computational, mathematical and experimental framework to overcome these challenges. To recapitulate early human brain development, the proposal will employ an in vitro human embryonic stem cell differentiation system. To obtain snapshots of the underlying gene regulatory network, high throughput single cell sequencing will be employed to obtain transcriptional profiles of thousands of single cells during the course of development. The challenge of inferring the sequence of cell states and cell state transitions will be overcome through a novel statistical method to obtain a joint probability distribution of the cell states, sequence of transitions and a key set of genes whose dynamics reflect these states and transitions. The inferences will be tested by mapping to in vivo data and using viral lineage tracing. The origins of forebrain and outer radial glial cells (oRG) progenitors uniquely enriched in the developing human forebrain will thus be determined. The challenge of building predictive models will be overcome by using methods from theoretical physics and ensemble modeling from statistics to build models that make probabilistic predictions. By using the available data as constraints on the model, the framework will extract joint probability distributions of all the parameters of the model. These distribution functions will then be used to produce probabilistic predictions about the responses of the underlying GRNs to perturbations. High probability predictions will be tested experimentally by perturbing gene expression and signaling during early brain development and the model will be iteratively improved. The success of this proposal will result in the first quantitative model of the gene regulatory network controlling the generation of forebrain and the oRG progenitor cells. If achieved, this work therefore would represent a major insight into the molecular and cellular events that give rise to the disproportionately gyrated human brain. Terms: <Assay><Bar Codes><Basal Transcription Factor><Basal transcription factor genes><Bioassay><Biochemical><Biological Assay><Birth Defects><Brain><Brain Nervous System><CRISPR><CRISPR approach><CRISPR based approach><CRISPR method><CRISPR methodology><CRISPR technique><CRISPR technology><CRISPR tools><CRISPR-CAS-9><CRISPR-based method><CRISPR-based technique><CRISPR-based technology><CRISPR-based tool><CRISPR/CAS approach><CRISPR/Cas method><CRISPR/Cas system><CRISPR/Cas technology><CRISPR/Cas9><CRISPR/Cas9 technology><Cas nuclease technology><Cell Body><Cell Communication and Signaling><Cell Line><Cell Signaling><CellLine><Cells><Cellular Morphology><Clustered Regularly Interspaced Short Palindromic Repeats><Clustered Regularly Interspaced Short Palindromic Repeats approach><Clustered Regularly Interspaced Short Palindromic Repeats method><Clustered Regularly Interspaced Short Palindromic Repeats methodology><Clustered Regularly Interspaced Short Palindromic Repeats technique><Clustered Regularly Interspaced Short Palindromic Repeats technology><Computational toolkit><Computer Analysis><Computing Methodologies><Congenital Abnormality><Congenital Anatomical Abnormality><Congenital Defects><Congenital Deformity><Congenital Malformation><Data><Development><Encephalon><Ethical Issues><Event><Expression Signature><Fore-Brain><Forebrain><Forebrain Development><Foundations><Gene Expression><Gene Expression Profile><Gene Transcription><General Transcription Factor Gene><General Transcription Factors><Generations><Genes><Genetic><Genetic Transcription><Glia><Glial Cells><Goals><Guide RNA><HLHX2><Human><In Vitro><Individual><Intracellular Communication and Signaling><Joints><Kolliker's reticulum><LH-2><LH2><LHX2><LHX2 gene><LIM HOX Gene 2><LIM Homeo Box Gene 2><LIM Homeobox Gene 2><Lentils><Lentils - dietary><Libraries><Maps><Math><Mathematics><Mesencephalon><Methods><Mid-brain><Midbrain><Midbrain structure><Mitotic><Modeling><Modern Man><Molecular><Neocortex><Neural Development><Neural Stem Cell><Neuroglia><Neuroglial Cells><Neurologic><Neurological><Non-Polyadenylated RNA><Non-neuronal cell><Nonneuronal cell><Organism><Physics><Probability><Progenitor Cells><Prosencephalon><RNA><RNA Expression><RNA Gene Products><Radial><Radius><Ribonucleic Acid><Role><SOX11><SOX11 gene><SRY-Box 11><SRY-Related HMG-Box Gene 11><Signal Transduction><Signal Transduction Systems><Signaling><Single cell seq><Statistical Methods><Strains Cell Lines><System><Techniques><Testing><Transcription><Transcription Factor Proto-Oncogene><Transcription factor genes><Viral><WNT Signaling Pathway><WNT signaling><Work><barcode><biological signal transduction><cell morphology><cell type><class development><computational analyses><computational analysis><computational methodology><computational methods><computational toolbox><computational tools><computational toolset><computer analyses><computer based method><computer based prediction><computer methods><computerized tools><computing method><course development><course material development><cultured cell line><developmental><developmental disease><developmental disorder><directed differentiation><disease model><disorder model><gRNA><gene expression pattern><gene expression signature><gene network><gene regulatory network><glial cell development><glial development><hESC><homotypical cortex><human ES cell><human ES cell lines><human ESC><human data><human embryonic stem cell><human embryonic stem cell line><human fetal tissue><human fetus tissue><human progenitor><human stem cells><human tissue><improved><in vivo><insight><isocortex><living system><machine learning based method><machine learning method><machine learning methodologies><model building><model organism><molecular biomarker><molecular marker><neocortical><neopallium><nerve cement><nerve stem cell><neural control><neural precursor><neural precursor cell><neural progenitor><neural progenitor cells><neural regulation><neurodevelopment><neuromodulation><neuromodulatory><neuron progenitors><neuronal progenitor><neuronal progenitor cells><neuronal stem cells><neuroprogenitor><neuroregulation><novel><predictive modeling><progenitor><progenitor cell differentiation><progenitor cell fate><progenitor differentiation><progenitor fate><prosencephalon development><response><single cell next generation sequencing><single cell sequencing><social role><statistic methods><statistics><stem and progenitor cell fate><stem and progenitor differentiation><stem cell differentiation><stem cell fate><stem cells><subventricular zone><success><time interval><tool><transcription factor><transcriptional profile><transcriptional signature>