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Module 3: Gene Expression/Proteomics

模块 3：基因表达/蛋白质组学

基本信息

批准号：
10700862
负责人：
Yutao Liu
金额：
$ 7.04万
依托单位：
AUGUSTA UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10700862
关键词：
Affect Aqueous Humor Awareness Bioinformatics Biological Markers Center Core Grants Consultations Cornea Data Data Analyses Development Devices Disease Disease Progression Experimental Designs Expression Profiling Funding Future Gene Chips Gene Expression Gene Expression Profile Gene Expression Profiling Gene Proteins Genetic Transcription Genomics Goals Mass Spectrum Analysis Measurement Oligonucleotide Microarrays Onset of illness Pathway Analysis Pathway interactions Post-Translational Protein Processing Preparation Proteome Proteomics Protocols documentation RNA analysis Research Personnel Retina Sampling Scientist Services Speed Spottings System Technology Tissues Trabecular meshwork structure Transcript Universities Validation Vision Vision Disorders Vision research Visual Visual System bead chip cDNA Arrays design experimental study high throughput technology insight instrument large datasets lens liquid chromatography mass spectrometry mass spectrometer new technology next generation sequencing novel protein expression single-cell RNA sequencing transcriptome transcriptome sequencing

项目摘要

The gene expression and proteomics module will provide investigators with expertise in designing and performing studies of global gene expression profiles and proteomic alterations related to diseases of the visual system. In the last two decades, a number of high throughput platforms have been developed to investigate global gene expression patterns. These technologies include spotted cDNA or oligonucleotide arrays, Affymetrix GeneChip microarrays, Illumina BeadChip microarrays, and most recently next-generation sequencing technologies including RNA sequencing (RNA-Seq) and Single-cell RNA sequencing (scRNA-seq). This Core will use RNA-Seq and scRNA-seq for gene expression studies. Similarly, the development of Liquid- Chromatography Mass Spectrometry (LC-MS/MS) over the last decade has revolutionized proteomic analyses through its unprecedented depth, speed and accuracy even with small volume samples. One of the latest breakthroughs in this technology is the Orbitrap Fusion Tribrid mass spectrometer, which allows researchers to extend the limits of proteomic analyses including post-translational modifications. We have this instrument at Augusta University and recently, we have successfully established a protocol for the analysis of the aqueous humor proteome using this device. These latest sequencing and mass-spec technologies, used to evaluate gene expression and protein levels, can be intimidating to researchers, whose scientific expertise lies elsewhere. The new technologies also present challenges associated with high throughput data analyses and interpretation. To circumvent these hurdles, the gene expression and proteomics module is intended to guide researchers in experimental design, sample preparation, thoughtful selection of appropriate technology, and bioinformatics support for sequencing and proteomic profiling. In addition, the core will also help researchers with the interpretation of results and designing further experiments for confirmation/validation of findings. The goal of this module is to guide vision researchers with high throughput gene expression analyses (using sequencing) and proteomic analyses (using mass-spectrometry) of ocular tissues. The services offered in this module reflect keen awareness of the nuances related to experimentation with specialized ocular tissues and will be accomplished in two aims. Aim 1: Provide core investigators with expert consultation, tailored to analysis of the visual system, on experimental design, sample preparation and selection of technology to conduct gene expression studies and/or proteomic analyses of ocular tissues. Aim 2: Provide core investigators with excellent bioinformatics support in analyzing the large data sets that are generated following high throughput gene expression or proteomic analyses of ocular tissues.

基因表达和蛋白质组学模块将为研究人员提供设计和