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UK funding (£348,442): 15 NSFBIO: Causal modeling of T cell signaling in time and space Ukri1 Oct 2016 UK Research and Innovation, United Kingdom
Overview
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15 NSFBIO: Causal modeling of T cell signaling in time and space
| Abstract | A great challenge in biomedical research is to understand how the regulation of cellular activation occurs in the interaction of dozens of signalling components. As most current research only addresses single components of signalling systems, new strategies to address entire signalling systems are required. With the dual objective to further develop methods for the investigation of complex signalling systems and to gain biological insight we study signal amplification in T cell activation by CD28. To explain what that means: T cells or T lymphocytes are central regulatory cells in the immune system. Their activation is critical in immune responses to pathogens, in cancer, and in autoimmune disease. For their activation they require two inputs. The first signal directly communicates the presence of a pathogen; the second signal, costimulation by CD28, communicates that other components of the immune system have recognised the same pathogen. Costimulation then amplifies the intracellular signalling processes triggered by the first signal. However, it remains unknown how this amplification is accomplished. Signal amplification, similar to many other signalling processes, is of great complexity, as many proteins need to collaborate. A critical component of such complexity is that proteins are rarely evenly distributed throughout cells but enrich at particular subcellular locations at particular times, thus generating complex spatiotemporal distributions. Co-enrichment of two proteins enhances their interaction efficiency. At the scale of many signalling proteins of a cell, spatiotemporal distributions thus determine how information flows through signalling networks in time and space thus regulating cellular function. Microscopy can determine the subcellular distributions of signalling proteins in live cells over time, a process referred to as imaging. Only when applied at a large scale as we uniquely do, imaging can capture the information flow across complex signalling systems as an efficient and unique means to understand the regulation of cellular function. To understand how CD28 amplifies T cell signalling, we will image signalling in T cells lacking CD28 engagement. As the imaging data thus acquired contains very large amounts of data, computational image analysis approaches are required. We develop such approaches collaboratively. Our work is part of a NSF/BBSRC US/UK binational pilot programme. Our US partner, the Murphy laboratory at Carnegie Mellon University, is supported by the NSF to develop advanced computational image analysis approaches for the imaging data we acquire. In combination large-scale imaging and computational image analysis are expected to reveal the mechanisms used by T cells to amplify signalling. Importantly, our strategy will be generally applicable to the analysis of complex signalling systems and thus can be transferred to the analysis of cellular activation in many other physiologically important settings. In addition, as signal amplification is wide spread, we also expect that data gained in T cells will inform mechanisms of signal amplification in other cell types. Understanding T cell signal amplification is also of medical interest. T cells are of great medical importance, particularly in autoimmune diseases and the immune response to cancer. In collaboration with groups at the University of Bristol and outside in academia and industry, we have begun to explore the role of signalling organisation in the autoimmune disease multiple sclerosis and its therapy and in primary immunodeficiency. Methods and data generated here will be transferred to these projects in the future. |
| Category | Research Grant |
| Reference | BB/P011578/1 |
| Status | Closed |
| Funded period start | 01/10/2016 |
| Funded period end | 30/09/2019 |
| Funded value | £348,442.00 |
| Source | https://gtr.ukri.org/projects?ref=BB%2FP011578%2F1 |
Participating Organisations
| University of Bristol |
The filing refers to a past date, and does not necessarily reflect the current state. The current state is available on the following page: University of Bristol, Bristol.
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