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: 10708803
• 负责人: Alexander Franks
• 金额: $ 19.89万
• 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/10708803
• 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.

蛋白质是细胞中分子活动的驱动力，但我们仍然缺乏对蛋白质的全面了解。 细胞调节蛋白质丰度的机制。一个长期存在的问题集中在 特别是转录后调节的机制和程度，这也决定了 mRNA水平可用于预测蛋白质丰度的程度。例如，mRNA水平 通常与基因间的蛋白质丰度相关，但mRNA-蛋白质相关性可能不同， 在不同的条件下，基因内的差异显著，部分原因是转录后调节，如 调节蛋白质的合成和降解。转录后调控已被大量分析 它是由异质细胞类型组成的样本，但在单细胞中仍基本未被探索。到 为了能够在单细胞分辨率下系统地分析转录后调控，我们需要一种新的 分析框架，其中1）占单细胞测量误差和技术偏差，这是 与mRNA和蛋白质丰度的相关生物信号卷积2）利用 概率模型，它汇集了功能组中基因之间的信息，或者解释了 决定蛋白质丰度的因素，如核糖体结合蛋白3）模型 单细胞mRNA和蛋白质数据中的丰度依赖性缺失数据和4）观察到的相关数据 后过渡期监管与可能的监管机制。为了实现这些目标，我们建立在 我们的长期合作，并提出以下目标： 目标1.建立单细胞转录后调控的推断方法。这些方法将 采用分层模型，该模型考虑了具有共同调控的基因之间的相似性， 机制等我们将显式地对不可验证的缺失数据进行建模，并考虑测量误差， 数据。 目标二。应用并验证目标1中的方法。我们将分析单细胞mRNA和蛋白质 来自人类免疫细胞和睾丸的测量，重点是识别和验证功能 由普通RNA结合蛋白调控的相关基因。 该研究将被纳入PI机构的教育计划， 关于顶点体验将为新工具开发可复制的软件。 相关性（参见说明）： 转录后调节的失调在人类癌症和其他疾病中非常常见， 通常它会影响特定的细胞亚群。尽管有证据表明转录后的重要性， 调节，它几乎完全未在单细胞水平上探索。拟议研究的目标是 开发新的方法来表征单细胞中的转录后调控。