NC3Rs | 20 Years: Pioneering Better Science
PhD Studentship

Using non-invasive in vivo imaging to address the 3Rs in high-throughput mouse phenotyping

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At a glance

Completed
Award date
September 2011 - September 2015
Grant amount
£120,000
Principal investigator
Dr Mark Lythgoe

Co-investigator(s)

Institute
University College London

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Contents

Application abstract

In the wake of the first draft of the full mouse genome sequence, large-scale mutagenesis programmes are underway that will produce mice with gene knockouts/point mutations for each of the approximately 25,000 genes in the mouse genome. Analysis of these mice in coming years will give new insights into the genetic basis of human disease and biology, as novel genes are identified that impact upon mammalian physiology and morphology. This studentship will develop non-invasive imaging of live mice to reduce the numbers of mice generated in high-throughput mutagenesis programs and start to refine experimental strategies for working with mouse models. We will use non-invasive in-vivo magnetic resonance imaging (MRI), to assess if this imaging technology can help us understand the function of genes within the brain, which will enable us remove major "bottlenecks" in the development of new diagnosis tests for genetic disease and human gene therapies.

Publications

  1. Ma D et al. (2019). Study the Longitudinal and Cross-Sectional Brain Volume Difference for Disease Progression and Treatment Effect on Mouse Model of Tauopathy Using Automated MRI Structural Parcellation. Frontiers in Neuroscience 13:11. doi: 10.3389/fnins.2019.00011
  2. Colgan N et al. (2017). Imaging of Tau Pathology Using Magnetic Resonance Imaging Textural Analysis. Frontiers in Neuroscience 11:599. doi: 10.3389/fnins.2017.00599
  3. O'Callaghan J et al. (2017). Tissue magnetic susceptibility mapping as a marker of tau pathology in Alzheimer's disease. Neuroimage 149:334-345. doi: 10.1016/j.neuroimage.2017.08.003
  4. Colgan N et al. (2016). Application of neurite orientation dispersion and density imaging (NODDI) to a tau pathology model of Alzheimer's disease. Neuroimage 125:739-44. doi: 10.1016/j.neuroimage.2015.10.043
  5. Holmes HE et al. (2016). Imaging the accumulation and suppression of tau pathology using multiparametric MRI. Neurobiology of Aging 39:184-94. doi: 10.1016/j.neurobiolaging.2015.12.001
  6. Powell NM et al. (2016). Fully-Automated μMRI Morphometric Phenotyping of the Tc1 Mouse Model of Down Syndrome. PLoS One 11(9):e0162974. doi: 10.1371/journal.pone.0162974
  7. Wells JA et al. (2015). In vivo imaging of tau pathology using multi-parametric quantitative MRI. NeuroImage 111:369-78. doi: 10.1016/j.neuroimage.2015.02.023
  8. Wells JA et al. (2015). Increased cerebral vascular reactivity in the tau expressing rTg4510 mouse: evidence against the role of tau pathology to impair vascular health in Alzheimer's disease. Journal of Cerebral Blood Flow and Metabolism 35(3):359-62. doi: 10.1038/jcbfm.2014.224
  9. O'Callaghan J et al. (2014). Is your system calibrated? MRI gradient system calibration for pre-clinical, high-resolution imaging. PLoS One 9(5):e96568. doi: 10.1371/journal.pone.0096568
  10. Ma D et al. (2014). Automatic structural parcellation of mouse brain MRI using multi-atlas label fusion. PLoS One 9(1):e86576. doi: 10.1371/journal.pone.0086576
  11. Roberts TA et al. (2014). In amnio MRI of mouse embryos. PLoS One 9(10):e109143. doi: 10.1371/journal.pone.0109143
  12. Campbell-Washburn A et al. (2013). Monitoring systemic amyloidosis using MRI measurements of the extracellular volume fraction. Amyloid 20(2). doi: 10.3109/13506129.2013.787984