Understanding how cells sort and transport proteins to the correct subcellular destination is central to cell biology and has direct implications for human disease. Our lab addresses this problem by combining cell biology with neuronal and metabolic disease models, structural and biophysical approaches, and computational tools including machine learning and AI-assisted protein design.
Fundamental discovery science and the secretory pathway
At the core of the group's work is the identification and characterisation of trafficking machinery. We dissect key sorting modules, including adaptor protein complexes, retromer, and the exocyst complex, using kinetic trafficking assays (RUSH), CRISPR/Cas9 screens, quantitative proteomics, lipidomics, and secretomics. These approaches allow us to capture the dynamics and regulation of Golgi-to-plasma-membrane transport, endosomal recycling, and Golgi organisation in living cells.
A major focus is the constitutive secretory pathway, the route by which most transmembrane and soluble proteins travel from the Golgi apparatus to the cell surface. This pathway delivers a wide variety of physiologically important cargoes, including cytokines, lipoproteins, antibodies, and hormones, yet the molecular machinery governing carrier formation, cargo selection, and membrane delivery remains poorly defined. We have identified the exocyst complex as an essential tethering factor for secretory carriers at the plasma membrane and have shown that its loss prevents the constitutive secretion of leptin by adipocytes and of antibodies by lymphocytes. We continue to dissect how post-Golgi transport carriers form, what determines their cargo content, and how they are targeted to the correct membrane domain.
Neuronal cell biology
We study membrane trafficking in neurons using human iPSC-derived neuronal models. This work addresses two related areas: the regulated secretory pathway and the trafficking events that underpin synaptic plasticity and long-term potentiation; and the sorting and processing of disease-relevant cargo. We have identified RABGAP1 as a novel regulator of amyloid precursor protein (APP) trafficking and processing, acting through its GTPase-activating protein activity to modulate Rab-dependent sorting at endosomal subdomains. Loss or overexpression of RABGAP1 alters APP processing in human neurons, with direct relevance to Alzheimer's disease. More broadly, we are interested in how adaptor proteins and Rab GTPases coordinate cargo sorting in the endolysosomal system, and how these mechanisms are disrupted in neurodegeneration.
Obesity, metabolism, and endocrinology
A substantial part of the group's work addresses the trafficking of metabolically important cargoes. In collaboration with Dr Daniel Fazakerley (Institute of Metabolic Science, Cambridge), we study the subcellular response to growth factors and insulin, including the intracellular sorting and surface delivery of the insulin-responsive glucose transporter GLUT4. We use AI-driven phenotypic screening to identify specific regulators of GLUT4 trafficking, with the aim of uncovering new biology relevant to insulin resistance and type 2 diabetes. In partnership with AstraZeneca, we are extending this work towards the discovery of new therapeutic approaches for metabolic disease. Our demonstration that loss of the exocyst complex prevents constitutive leptin secretion from adipocytes connects the fundamental biology of the secretory pathway directly to metabolic and endocrine physiology, and we are pursuing how defects in secretory machinery contribute to metabolic disease more broadly.
Technology development and collaborations
We actively develop and apply advanced technologies across the group's biological questions. In collaboration with Prof Steven Lee (Yusuf Hamied Department of Chemistry), we have developed methods for volumetric single-molecule tracking inside subcellular structures, enabling us to follow individual protein molecules in three dimensions within living cells. With Prof David Owen and Dr Bernard Kelly (CIMR), we combine structural biology, biophysics, and AI-assisted protein binder design to understand sorting machinery at atomic resolution and to develop new tools for probing and perturbing trafficking pathways. We also work closely with Prof Margaret Robinson (CIMR) on adaptor protein biology.
Across these collaborations, the lab integrates machine learning phenotypics, lattice-SIM and correlative light-electron microscopy, and structural approaches alongside its core cell-biological toolkit.
Approaches
Confocal and super-resolution microscopy (including lattice-SIM), single-molecule biophysics, correlative light and electron microscopy, CRISPR-based genetic screens, AI-driven phenotypic screening, AI-assisted binder design, quantitative proteomics, lipidomics, secretomics, and human iPSC-derived neuronal models.
