Collaborative Research: DMS/NIGMS 2: Methods for Systematic Analysis of 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 细胞调节蛋白质丰度的机制。一个长期存在的问题集中在 特别是转录后调控的机制和程度，这也决定了 信使核糖核酸水平可用于预测蛋白质丰度的程度。例如，信使核糖核酸水平 通常与不同基因间的蛋白质丰度有关，但这种信使核糖核酸与蛋白质的相关性可能有所不同。 在不同条件下显著存在于基因内，部分原因是转录后调控，例如 蛋白质合成和降解的调节。转录后调控已经被大量分析 样本由不同类型的细胞组成，但在很大程度上仍未在单个细胞中进行探索。至 为了能够在单细胞分辨率下对转录后调控进行系统分析，我们需要一种新的 分析框架，它1)解释了单元格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)杠杆 概率模型，它将信息集中在功能群中的基因上，或解释 影响蛋白质丰度的决定因素，如核糖体结合蛋白模型 单细胞mRNA和蛋白质数据中依赖丰度的缺失数据和4)观察到的关联 转录后调控与可能的调控机制。为了实现这些目标，我们在 我们的长期合作，并提出以下目标： 目的1.建立单细胞转录后调控的推断方法。这些方法将 使用分层模型来解释具有共同调控的基因之间的相似性 机械装置。我们将对不可忽略的缺失数据进行显式建模，并在 数据。 目的2.应用和验证目标1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 受常见RNA结合蛋白调控的相关基因。 这项研究将被纳入国际和平研究所的教育计划，并特别注重 关于顶石体验。将为新工具开发可重复使用的软件。 相关性(请参阅说明)： 转录后调控失调在人类癌症和其他疾病中非常常见，而且 通常它会影响特定的细胞亚群。尽管有证据表明转录后基因的重要性 在调控方面，它几乎完全没有在单细胞水平上被探索。拟议研究的目标是 开发表征单细胞转录后调控的新方法。

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.