Vibrational Stratification of Remyelination: A Rapid Selection Process for New MS Therapies

Precision Medicine Project - Vibrational Stratification of Remyelination: A Rapid Selection Process for New MS Therapies

Supervisor(s): Prof Alison Hulme, Prof Anna Williams & Dr Peter Bankhead
Centre/Institute: School of Chemistry

Background

In multiple sclerosis (MS), patches of demyelination occur where the myelin sheaths are damaged and stripped off the nerves. This causes neurological symptoms such as paralysis, sensory changes and blindness. Replacement of these myelin sheaths (remyelination) can happen in MS, which can restore nerve function. This is generally inefficient, and often a scar forms instead, causing a long-term problem for the patient. Remyelination is carried out in part by by oligodendrocyte precursor cells which are attracted to the damaged area, make contact with nerves, mature and form myelin sheaths to replace those that are damaged. A failure of remyelination can occur with a problem at any one of these steps. 

Remyelinated myelin around neuronal axons is identified by electron microscopy as thinner than undamaged myelin, but this cannot be seen reliably with conventional or even super resolution immunofluorescence, making identification of remyelination slow, expensive and only feasible on fixed tissue [1]. Whilst other label-free platforms indicate the presence of different cellular/tissue components (e.g. through refractive index changes), they provide no information about their chemical make-up. In hyperspectral stimulated Raman scattering (hSRS), pseudo-Raman spectra are generated for every pixel within the field of view with superb spatial resolution. hSRS data is quantitative and correlates with spontaneous Raman; it allows the analysis of multiple components per pixel and can be used to discriminate cell types, or follow changes in response to stimuli [2]. It has been used to track lipid variance in lipid droplets, and to analyse the myelin sheath of neurons. 

Aims

  • To use hSRS for label-free identification of lipid changes in demyelination and myelin repair in fixed tissue and live brain slice models [3]; enhancing our understanding of diseases by studying individual steps in the process directly, such as “myelin swelling” that we have identified occurs during the process of demyelination.
  • To develop a new bioimage analysis strategy using image processing and machine learning techniques to assess myelin growth from hSRS images [4]. Training the algorithm using two photon fluorescence (TPF) and Raman immunohistochemistry obtained concurrently on our state-of-the-art coherent Raman multiphoton confocal microscope using a series of bespoke chemical tools.
  • To identify vibrational signatures in myelin correlating with differences such as age, sex and presence of MS that might help to explain vulnerability to neurodegeneration in MS and other similar diseases, variation in response to pro-remyelination therapies, and a Precision Medicine approach to treatment. 

Training outcomes

The student will gain an interdisciplinary training in the use of imaging (particularly coherent Raman microscopy) to understand stratification in MS and similar diseases. They will be trained to relate spectral signatures to biochemical changes and to use these signatures to assess responses to therapies. The student will gain new analytical skills in handling large datasets (such as complex images) and will develop new methods for image analysis using AI. 

References 

  1. ‘Remyelination in animal models of multiple sclerosis: finding the elusive grail of regeneration’: D. Packer, E. E. Fresenko, E. P. Harrington, Front. Mol. Neurosci., 2023, 16, 1207007.
  2. ‘Recent advances in the use of stimulated Raman scattering in histopathology’: M. Lee, C. S. Herrington, M. Ravindra, K. Sepp, A. Davies, A. N. Hulme, V. G. Brunton, Analyst, 2021, 146, 789-802.
  3. ‘Protocol for assessing myelination by human iPSC-derived oligodendrocytes in Shiverer mouse ex vivo brain slice cultures’: T. M. Tsarouchas, L. Zoupi, A. Williams, E. M. Gibson, STAR Protocols, 2025, 6, 103609.
  4. ‘AimSeg: A machine-learning-aided tool for axon, inner tongue and myelin segmentation’: P. Carrillo-Barberà, A. M. Rondelli, J. M. Morante-Redolat, B. Vernay, A. Williams, P. Bankhead, PLoS Comput. Biol., 2023, 19, e1010845.

Apply Now

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  • The deadline for 26/27 applications is Monday 12th January 2026
  • Applicants must apply to a specific project. Please ensure you include details of the project on the Recruitment Form below, which you must submit to the research proposal section of your EUCLID application.
  • Please ensure you upload as many of the requested documents as possible, including a CV, at the time of submitting your EUCLID application.  

Q&A Sessions

Supervisor(s) of each project will be holding a 30 minute Q&A session in the first two weeks of December. 

If you have any questions regarding this project, you are invited to attend the session on TBC via Microsoft Teams. Click here to join the session.

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