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.

蛋白质是细胞中分子活性的驱动因素，但我们仍然缺乏对 细胞调节蛋白质丰度的机制。一个长期存在的问题集中在 特别是关于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 通常与基因之间的蛋白质丰度相关，但是mRNA-蛋白相关性可能会有所不同 在跨条件跨基因内显着，部分归因于转录后调节，例如 调节蛋白质合成和降解。转录后调节已在批量上进行分析 由异质细胞类型组成的样品，但在单个细胞中仍未探索它。到 在单细胞分辨率下对转录后调节进行系统分析，我们需要一种新颖的 分析框架1）说明单细胞测量误差和技术偏见， 与mRNA和蛋白质丰度相关的生物学信号卷积2） 概率模型，这些模型在官能团中跨基因汇总信息或说明 有助于蛋白质丰度的决定因素，例如核糖体结合蛋白3）模型 单细胞mRNA和蛋白质数据中的丰度依赖性丢失数据以及4）观察到的同伴 转录后调节可能具有调节机制。为了实现这些目标，我们基于 我们长期的合作并提出以下目的： 目的1。开发用于推断单个细胞转录后调节的方法。这些方法将 采用层次模型，这些模型说明基因与常见调节的基因之间的相似性 机制。我们将明确对不可忽视的丢失数据进行建模，并在 数据。 目标2。要应用和验证AIM 1的方法。我们将分析单细胞mRNA和蛋白质 人类免疫细胞和睾丸的测量，重点是识别和验证功能 由常见RNA结合蛋白调节的相关基因。 该研究将纳入PI机构的教育计划，特别关注 关于顶峰的经验。将开发针对新工具的可再现软件实现。 相关性（请参阅说明）： 在人类癌症和其他疾病中，转录后调节的失调非常常见，并且 通常会影响细胞的特定子集。尽管有证据表明转录后的重要性 法规，它几乎在单细胞水平上几乎没有探索。拟议研究的目的是 开发新方法来表征单个细胞中的转录后调节。