PhD Studentship - Advancing Women's Health Research with Next-Generation Microfluidic Technologies to Identify Ovarian Cancer Disease Signatures

No. of positions: 1

This 3.5 year PhD project is fully funded; tuition fees will be paid and you will receive an annual tax free stipend set at the UKRI rate (£19,237 for 2024/25). We expect this to increase each year. This PhD project is for home students.

PhD Project Brief: Advancing Ovarian Cancer Diagnostics with Microfluidic Technologies

Overview: Ovarian cancer causes 150,000–200,000 deaths annually, primarily due to delayed diagnosis from subtle early symptoms. Current diagnostics are costly and inefficient, relying on pelvic exams, imaging, blood tests, and biopsies. This project develops a microfluidic platform mimicking the gynecological tract to study early-stage ovarian cancer markers, specifically serous tubal intraepithelial carcinoma (STIC) lesions. Leveraging real-time observation, non-invasive sampling, and computational modeling, the system aims to improve early detection and survival rates.

Approach: This interdisciplinary research integrates microfluidics, super-resolution microscopy, and patient-derived organoids to replicate natural flow conditions and study host-microbiome interactions. Clinical microbiome and omics data inform device design and experiments, with fabrication optimized at the National Graphene Institute. The platform facilitates physical and digital modeling of the upper reproductive tract to uncover diagnostic biomarkers. Preliminary work with endometrial and fallopian tube cell lines demonstrates feasibility.

Research Objectives:

1.Host Response to STIC Lesions
Develop a microfluidic chip replicating the fallopian tube environment to study STIC lesions using patient-derived organoids. Analyse cell health, stress responses, and metabolomic data, refining the model with bloodwork and vaginal swabs.

2.Impact of STIC Lesions on the Vaginal Microbiome
Investigate microbiome changes during STIC progression by linking fallopian tube and vaginal microfluidic models. Employ CLASI-FISH imaging and multi-omics to uncover disease signatures.

3.Computational Model of Ovarian Cancer Progression
Create a predictive model integrating microfluidic, clinical, and omics data to identify early-stage disease indicators.

Expected Impact: This project combines microfluidics, imaging, and computational tools to establish early diagnostic methods for ovarian cancer. It promises to advance women’s health diagnostics, reduce delays in detection, and enable life-saving interventions. Objectives can be tailored to suit the candidate's interests and methodologies

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.

To apply, please contact Dr Raveen Tank - raveen.tank@manchester.ac.uk. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.

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