Sensory information is processed by neuroendocrine gene networks to regulate organismal physiology. During this process, communications between the brain and other tissues produce sophisticated computations that transform complex sensory inputs to a coordinated set of physiological responses needed for health and survival. In contrast, defects in these communications lead to diabetes, infection, neurodegeneration, cancer, and many other diseases. Understanding the connectivity of these neuroendocrine gene networks will reveal their computational functions and the input-output relationships that connect environment to physiology or disease.
This project will address these questions with single-cell transcriptomics to characterise the expression of every gene in every cell in the brain and body. Since neuroendocrine signals are highly conserved, we will perform well-defined single-cell studies in the roundworm C. elegans, the only animal where every individual cell is identified. Multifaceted sensory inputs (e.g., diet, microbiome, temperature, pheromones) are processed by these networks to control diverse physiological outputs (e.g., development, immunity, metabolism, ageing). Thus, the student can co-create this project by selecting specific sensory cues and physiological outputs for in-depth research that best match their interests.
The student will learn single-cell transcriptomics, molecular genetics, single-molecule imaging, physiological assays, bioinformatics, image analysis, and machine learning/AI. This training will facilitate the development of systems thinking needed to solve complex problems.
The overall goal is to elucidate the connectivity of the neuroendocrine gene network that links selected sensory cues to physiological responses at single-cell resolution, providing a “connectome” for gene regulation across the brain and body.
Year 1: Single-cell transcriptomics to reveal gene network activity that encode sensory cues in neurons and other tissues. Computationally infer network connections. Training in molecular genetics, bioinformatics, programming, machine learning/AI, and image analysis.
Year 2: Computational predictions of phenotypes in mutants. Develop machine learning/deep learning pipeline for image analysis of microscopy data. Use this pipeline to validate transcriptomic data with fluorescence microscopy of Hybridisation Chain Reaction (HCR3.0) to visualise specific transcripts.
Year 3: Validate the effects of gene-environment interactions on physiological outputs by testing mutants in neuroendocrine pathways. Relate environmental inputs to physiological outputs.
Year 4: Complete experimental work. Write thesis.
During the rotation, students will use HCR3.0 to count single molecules of mRNA to measure gene expression in wild-type and mutants that disrupt specific neuroendocrine signals. They will apply machine learning and image processing to automate this image analysis. There will also be opportunities for bioinformatic and network analysis of single-cell transcriptomic datasets.