Quantitative Analysis of Single Cell Learning

学习被认为需要大脑，但即使是非常简单的动物也有学习的能力。即使是单个细胞也显示出原始类型的学习，但在没有神经系统的情况下，这种学习是如何发生的，目前还不清楚。在这个项目中，一个巨大的单细胞生物体Stentor将被用来探索单细胞如何学习。Stentor细胞在它们的自然栖息地被捕食，但当被触摸时可以收缩成球来逃脱攻击，但这种收缩会消耗能量。对于位于池塘植物上的Stentor细胞来说，它经常会被池塘植物或不具威胁性的小藻类利用。在决定触摸时是否收缩时，Stentor细胞依赖于过去的经验。这些细胞学会忽略轻微的、非威胁性的触摸，只有在受到更大的攻击力打击时才会收缩。以类似的方式，生活在铁轨旁的人类习惯了火车，当他们听到火车经过时，他们不会跳起来。这种学习在所有动物身上都可以看到，但通常在那些有神经系统的动物身上表现出来。单细胞能学习吗？如果是这样的话，是如何做到的呢？在实验室中培养的单个Stentor细胞将被拍摄下来，当它们对机械力做出反应时收缩，当不同的基因被关闭时，反应将被测量。这将揭示细胞是如何在分子水平上学习的。与此同时，一个简单的行为数学模型将被用来：a)预测与触摸时的感觉有关的基因；b)识别与收缩有关的基因；c)确定细胞如何决定是否收缩。这个项目将第一次展示单个细胞是如何学习的。更广泛的影响活动将包括对学生进行跨学科培训以及公共宣传活动。细胞整合多个输入，并在不同的行为反应中进行选择，在某些情况下，似乎是从经验中学习。细胞处理信息以产生适当行为的计算过程仍然知之甚少。学习通常被认为是具有某种形式的神经网络的多细胞动物的特征，但单细胞的学习能力似乎表明它是生命中更普遍的特征。用单个细胞研究学习的最容易处理的系统之一是Stentor Blue uleus，这是一个巨大的细胞，它显示出对机械刺激的可量化行为。重复刺激会导致习惯化，即细胞学会忽略特定大小的刺激。Stentor的习惯化已经有了很好的记录，但其机制基础尚不清楚。在这个项目中，一位蓝柱体生物学专家将与一位计算生物学专家合作，对学习是如何在单个细胞中发生的进行量化理解。该项目将把细胞反应的定量测量与细胞学习和基因功能的分子扰动的简单两态数学模型结合起来，提出关于学习如何发生的基本问题，确定细胞学习和记忆的关键分子途径，并探索细胞决策的计算复杂性。基因功能的研究将利用蛋白质组和磷蛋白质组信息来识别感觉和效应器分子，以及将刺激与反应联系起来的信号连接。一旦知道了这些因素，就有可能确定系统的哪些方面(感觉、效应器或信号)在学习过程中受到调节。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Learning is assumed to require a brain, but even very simple animals are capable of learning. Even single cells have been shown to display primitive types of learning, but how such learning takes place, without a nervous system, is currently not understood. In this project, a giant single cell organism, Stentor, will be used to explore how a single cell can learn. Stentor cells are preyed upon in their natural habitat but can escape from attack by contracting into a ball when touched, but this contraction burns up energy. For a Stentor cell sitting on a pond plant, it will often get tapped by pond plants or small algae that are not threatening. In deciding whether or not to contract when touched, the Stentor cell relies on past experience. The cells learn to ignore light, non-threatening touches, and only contract when hit with a larger aggressive force. In an analogous way, humans living by a railroad track get used to the train and they don’t jump when they hear it go by. This kind of learning is seen in all animals, but it is usually displayed in those with a nervous system. Can single cells learn? If so, how? Single Stentor cells grown in the lab will be videotaped as they contract in response to a mechanical force, and the response will be measured when different genes are shut down. This will reveal how the cell learns at a molecular level. At the same time, a simple mathematical model of behavior will be used to: a) predict genes that are involved in sensing when touched; b) identify genes that are involved in driving the contraction; and c) identify how the cell decides whether or not to contract. This project will show, for the first time, how a single cell is able to learn. Broader Impact activities will include the interdisciplinary training of students along with public outreach activities. Cells integrate multiple inputs and select between different behavioral responses, in some cases seeming to learn from experience. The computational processes by which cells process information to generate appropriate behaviors remain poorly understood. Learning is usually considered to be a feature of multicellular animals with some form of neuronal network, but the seeming ability of single cells to learn suggests it is a more general feature of life. One of the most tractable systems for studying learning by a single cell is Stentor coeruleus, a giant cell that shows quantifiable behaviors in response to mechanical stimulation. Repeated stimulation leads to habituation, in which the cell learns to ignore a stimulus of a particular magnitude. Habituation in Stentor has been well documented, but the mechanistic basis is unknown. In this project, an expert on the biology of Stentor coeruleus will team up with an expert on computational biology, to develop a quantitative understanding of how learning takes place in a single cell. The project will combine quantitative measurements of cell responses with a simple two-state mathematical model for cellular learning and molecular perturbations of gene function, to ask fundamental questions about how learning takes place, identify key molecular pathways that underlie learning and memory in a cell, and probe the computational complexity of cellular decision-making. Investigation of gene function will exploit proteomic and phosphoproteomic information to identify the sensory and effector molecules along with the signaling connections that link the stimulus to the response. Once these elements are known, it will then be possible to determine which aspects of the system (sensory, effector, or signaling) are modulated during the learning process.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.