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PhD Studentship: Raman Spectroscopy for Early On-Farm Diagnosis of Nephrocalcinosis (Kidney Stone Disease) - The #1 Issue of Concern for Salmon and Trout Aquaculture

University of Exeter - Biosciences

Qualification Type: PhD
Location: Devon, Exeter
Funding for: UK Students, EU Students, International Students
Funding amount: The studentship will provide funding of fees and a stipend of £21,805 per annum for 2026/7
Hours: Full Time
Placed On: 27th October 2026
Closes: 8th December 2026
Reference: 6009

Rainbow trout and Atlantic salmon represent the gold standard for technologically advanced fish farming (aquaculture). The market is valued at $5.3 billion for trout globally (£59 million in the UK) and $18.9 billion for salmon globally (£1.13 billion in the UK). An emerging but quantitatively important disease problem in aquaculture is nephrocalcinosis (‘kidney stones’ formed by precipitation of calcium minerals). This is now the #1 issue of concern within trout producers for all 4 categories surveyed (‘mortality’, ‘reduced growth’, ‘reduced-welfare’ and ‘increasing problem’), and ranked 1st, 2nd or 3rd for these categories for salmon (1). Nephrocalcinosis in fish is associated with sub-optimal water chemistry in aquaculture.

The key variables are elevated dissolved CO2 associated with intensive production, but elevated calcium, salinity, phosphate, and alkalinity in the water also play a role. The digestive process, regardless of diet composition, induces major changes in blood acid-base chemistry in fish (2) that also translate into urine chemistry changes that can increase the likelihood of mineral precipitation within the kidney (3). Some diet ingredients can further exacerbate kidney stone formation in both fish and humans (4). A key limitation for improving fish welfare and aquaculture productivity with respect to this disease is the lack of early and cost-effective diagnosis of nephrocalcinosis.

However, coherent Raman scattering microscopy – including Stimulated Raman Scattering (SRS) and Broadband Coherent Anti-Stokes Raman Scattering (BCARS) – is proving its value for label-free, chemically-specific diagnosis of calcification related pathologies in humans, including calcification associated with breast cancer (5). Recent progress with this technology suggests strong opportunities for diagnosing and quantifying calcium-based mineral deposits in animal tissues WITHOUT fixation OR the need for tissue staining, generating hyperspectral image data that maps tissue chemistry at sub-micron resolution. This raises the potential for small hand-held or bench top devices for use at farm sites, eliminating delays and diagnostic difficulties associated with send-away lab samples that need fixing and staining. Machine learning will be central to converting these complex spectral image data into simple, actionable outputs for industry – for example automated classification Engineering and Physical Sciences Research Council Doctoral Landscape Award and quantification of mineral deposits – enabling rapid diagnosis of pathologies and health problems associated with nephrocalcinosis.

We have existing strong collaborative relationships with both trout and salmon producers in the UK (e.g. British Trout Association, Kames, MOWI, Cooke Aquaculture Scotland) and plan to use fish tissue samples obtained from their sites as part of this project. In addition, we will conduct in vivo experiments with fish at the Aquatic Resources Facility (Biosciences) at Exeter that mimic the conditions linked to nephrocalcinosis to generate kidney tissue samples from known origins. Together these samples will allow the PhD student to explore disease aetiology and test the efficacy of Raman spectroscopy as a rapid diagnostic tool for nephrocalcinosis, and explore options for cost-effective analysis on site at aquaculture sites.

References:

  1. Moldal et al (2024) Fish Health Report (www.vetinst.no)
  2. Cooper & Wilson (2008) J Experimental Biology, 211(15):2542-2550
  3. Emadi et al (2026) Aquaculture, 613: 743354
  4. Crivelli et al (2021) Nutrients, 13:62
  5. Bouzy et al (2023) Analytical Methods15(13):1620-1630
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