Collaborative Research: DMS/NIGMS 2: Methods for Systematic Analysis of Post-transcriptional Regulation in Single Cells

合作研究：DMS/NIGMS 2：单细胞转录后调控的系统分析方法

• 批准号: 10492773
• 负责人: Alexander Franks
• 金额: $ 20.38万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA BARBARA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-23 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10492773
• 关键词: Affect; Binding Proteins; Biological; Cells; Collaborations; Computer software; Data; Disease; Education; Genes; Goals; Human; Immune; Institution; Instruction; Malignant Neoplasms; Measurement; Messenger RNA; Methodology; Methods; Modeling; Molecular; National Institute of General Medical Sciences; Post-Transcriptional Regulation; Protein Biosynthesis; Proteins; RNA-Binding Proteins; Regulation; Reproducibility; Research; Resolution; Ribosomes; Sampling; Signal Transduction; Statistical Models; Testis; cell type; experience; functional group; novel; programs; protein degradation; tool

Proteins are the drivers of molecular activity in the cell, but we still lack a comprehensive understanding of the mechanisms by which cells regulate protein abundances. A long-standing question has focused specifically on the mechanisms and degree of post-transcriptional regulation, which also determines the degree to which mRNA levels can be used to predict protein abundances. For example, mRNA levels generally correlate with protein abundance across genes, but that mRNA-protein correlations can vary significantly within genes across conditions, due in part to post-transcriptional regulation, such as regulation of protein synthesis and degradation. Post-transcriptional regulation has been analyzed in bulk samples composed of heterogeneous cell types, but it remains largely unexplored in single cells. To enable systematic analysis of post-transcriptional regulation at single-cell resolution, we need a novel analytic framework which 1) accounts for single-cell measurement error and technical bias, which are convolved with the relevant biological signal for both mRNA and protein abundance 2) leverages probabilistic models which pool information across genes in functional groups or account for the determinants contributing to protein abundance, like ribosomal binding proteins 3) models abundance-dependent missing data in single-cell mRNA and protein data and 4) associates observed post-transcriptions regulation with likely regulatory mechanisms. To achieve these goals, we build upon our long-standing collaboration and propose the following aims: Aim 1. To develop methods for inferring post-transcriptional regulation in single cells. These methods will employ hierarchical models which account for similarities between genes with common regulatory mechanisms. We will explicitly model non-ignorable missing data and account for measurement error in the data. Aim 2. To apply and validate the methodology from Aim 1. We will analyze single-cell mRNA and protein measurements from human immune cells and testis with a focus on identifying and validating functionally related genes regulated by common RNA binding proteins. The research will be integrated into the education programs at the PI's institutions, with a particular focus on capstone experiences. Reproducible software implementations for new tools will be developed. RELEVANCE (See instructions): Dysregulation of post-transcriptional regulation is very frequent in human cancers and other diseases, and usually it affects specific subsets of cells. Despite evidence for the importance of post-transcriptional regulation, it is almost completely unexplored at single-cell level. The goal of the proposed research is to develop new methodologies for characterising post-transcriptional regulation in single cells.

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