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University of Cambridge

Development of an Optogenetic Toolkit for the Interrogation of T cell Signalling Dynamics

Abstract

dc:description.abstract

T cells are a cornerstone of the mammalian adaptive immune system. A range of T-cell subsets exist that can orchestrate the overall immune response to pathogens or cancers, either by directly killing infected cells or licensing other cells to do so. Dysregulation of this important process can result in immunodeficiency or autoimmunity. Although T cells have been studied extensively over many decades, the detailed mechanisms underlying T-cell activation remain to be fully resolved. This thesis describes the development of new optogenetic approaches for the modulation of T-cell signalling dynamics and the interrogation of key events in T-cell activation to help investigate this question. Optogenetics is a rapidly emerging technique whereby light can be used to control the spatial and temporal activation, or inactivation of signalling pathways at unprecedented resolution. The methods described in this work utilise the blue light-responsive LOV2 photo-domain from the common oat A. Sativa, which is the foundation of the both the ‘LOVTRAP’ and ‘TULIPs’ optogenetic toolkits. T-cell antigen receptor (TCR) microclusters arise early during the interaction between T cells and antigen presenting cells (APCs). These TCR signalling platforms contain the proteins necessary for sustained T-cell activation, yet the processes underlying their formation and dissociation are still not fully characterised as they have been difficult to investigate with current chemical and genetic manipulations of T cells. Using two optogenetics systems combining either LOVTRAP or TULIPs and the microcluster- scaffolding protein LAT (Linker for the Activation of T cells), it was possible to modulate early T-cell signalling events and measure functional outputs in real-time. Unfortunately, the biological limitations of these LAT-based systems meant that they could not be used to quantitatively investigate microcluster formation. However, in an alternative approach, a drug-inducible, light-controllable chimeric antigen receptor was successfully developed that yielded important new insights into the rapid rate of signal decay within the TCR signalling pathway and the temporal dynamics of T-cell activation over several timescales. T cell-dependent bispecific antibodies (TDBs) are a new class of immuno-therapeutics that can specifically direct a T-cell response towards tumours, by crosslinking the TCR complex to a surface- expressed target on the cancerous cells. However, their mechanism of action has not been studied in detail. The close apposition of the T cell and target cells driven by the TDB interaction can result in the steric exclusion of phosphatases, such as CD45, away from the TCR at the TDB-generated cell-cell interface due to their large, rigid extracellular domains. Using the myeloma-expressed antigen, FcRH5, it was found that membrane-proximal epitopes of FcRH5 drive more robust TCR clustering and increased CD45 exclusion than membrane distal epitopes, which strongly correlated with effective killing of the target cell. These findings have important implications for therapeutic design and implementation of TDBs.

Degree

thesis:*
Name dc:type.qualificationname
Doctor of Philosophy (PhD)
Level dc:type.qualificationlevel
Doctoral
Grantor dc:publisher.institution
University of Cambridge
Year dc:date.issued
2018

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Harris, Michael James
Advisor dc:contributor.advisor
  • James, John Robert

Subjects

dc:subject × 10

Rights

dc:rights
Language dc:language
en

Identifiers

dc:identifier.*
OAI identifier oai:identifier
oai:www.repository.cam.ac.uk:1810/274251

Chain of custody

source
Harvested from
Cambridge University
Base URL
api.repository.cam.ac.uk/server/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Harris, Michael James. Development of an Optogenetic Toolkit for the Interrogation of T cell Signalling Dynamics. Doctoral thesis, University of Cambridge, 2018. https://doi.org/10.17863/CAM.21363