ILLUMINATING CELLULAR INDIVIDUALITY THROUGH BACTERIOPHAGE INFECTION

通过噬菌体感染阐明细胞个性

基本信息

批准号：
10442380
负责人：
Ido Golding
金额：
$ 34.73万
依托单位：
UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2026-06-30
项目状态：
未结题

项目摘要

PROJECT SUMMARY / ABSTRACT A key goal of biophysics is to predict the behavior of living systems. This goal is hampered by the fact that, when examined at the single-cell level, this behavior appears largely unpredictable: Genetically identical cells, within a uniform environment, exhibit heterogeneous phenotypes in terms of gene expression, signaling, and consequent fate choice. This cellular individuality is observed throughout biology, from the emergence of antibiotic resistance among bacteria, to cell differentiation in the early mammalian embryo, and numerous other examples. Studies over the last two decades have pinpointed the stochastic origins of cellular individuality, by demonstrating that the inherent randomness (“noise”) of single-molecule events can be amplified into protein number fluctuations at the cellular level. The picture that emerged from those studies is of living cells as “noisy machines”, incapable of high precision, whose fate choices are subject to significant randomness. But the widespread success of the “noise” concept in describing cellular heterogeneity also points to its weakness: It is easy to describe single-cell properties as “stochastic” and map them into statistical distributions, but doing so does not mean that we understand the underlying cellular process. On the contrary, by creating a façade of understanding, a stochastic description may impede our efforts to uncover the deterministic factors that drive single-cell behavior. Recent years have seen a growing awareness of this caveat and an increase in efforts to identify the deterministic drivers (so-called “hidden variables”) of cellular individuality, but it is fair to say that we still lack a satisfactory picture for what drives single-cell behavior even in the simplest systems, to say nothing of more complex ones. Research goal. The choice between rapid cell death (lysis) and viral dormancy (lysogeny), following infection of E. coli by bacteriophage lambda, serves as a paradigm for the way genetic networks drive cell fate decisions, and for the purported role of molecular randomness in this process. Building on our work over the last decade, we will use lambda infection to identify hidden drivers of cellular individuality in gene regulation and fate choice. By revealing how deterministic the decision process is, we aim to establish lambda as a paradigm for precise— rather than “noisy”—cell fate choice. In parallel to the work on lambda, we will continue to develop tools for the manipulation, imaging, analysis, and modeling of individual cells, and apply them in collaborative projects addressing cellular individuality across diverse biological contexts.

项目概要/摘要生物物理学的一个关键目标是预测生命系统的行为，但这一目标受到以下事实的阻碍：在单细胞水平上进行检查，这种行为似乎在很大程度上是不可预测的：基因相同的细胞，在统一的环境，在基因表达、信号传导和表达方面表现出异质表型从细胞出现开始，这种细胞个体性就在整个生物学中被观察到。细菌的抗生素耐药性、早期哺乳动物胚胎的细胞分化等例子。过去二十年的研究已经确定了细胞个体的随机起源，证明单分子事件固有的随机性（“噪声”）可以放大到蛋白质中这些研究得出的结论是，活细胞是“嘈杂的”。机器”，无法高精度，其命运选择具有很大的随机性。 “噪声”概念在描述细胞异质性方面的广泛成功也指出了它的弱点：很容易将单细胞特性描述为“随机”并将其映射到统计分布中，但这样做并不意味着我们了解了潜在的细胞过程，相反，通过创建一个外观。理解，随机描述可能会阻碍我们揭示驱动因素的确定性因素的努力近年来，人们越来越意识到这一警告，并加大了努力。确定细胞个体的确定性驱动因素（所谓的“隐藏变量”），但可以公平地说，我们即使在最简单的系统中，仍然缺乏驱动单细胞行为的令人满意的图像，更不用说更复杂的。研究目标：感染后细胞快速死亡（裂解）和病毒休眠（溶源）之间的选择。大肠杆菌噬菌体 lambda 是遗传网络驱动细胞命运决定方式的范例，以及分子随机性在这一过程中的所谓作用，以我们过去十年的工作为基础，我们将利用 lambda 感染来识别基因调控和命运选择中细胞个体的隐藏驱动因素。通过揭示决策过程的确定性，我们的目标是将 lambda 建立为精确的范例：而不是“嘈杂”——细胞命运选择在 lambda 工作的同时，我们将继续开发用于细胞命运选择的工具。单个细胞的操作、成像、分析和建模，并将其应用于协作项目解决不同生物背景下的细胞个性。