Quantitative Analysis of Single Cell Learning

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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.