QIMR Berghofer Medical Research Institute Winter Research projects
Generation of a line of mosquitoes persistently infected with insect-specific viruses
Hours of engagement | 36 |
Location | Herston: QIMR Berghofer |
Project description | Arboviruses of mosquitoes represent a global health burden which is not adequately met by available treatments. As such, limiting arbovirus infections largely hinges on vector control. However, several factors threaten to expand vector distributions and outstrip existing vector control strategies. Insect-specific virus (ISVs) have gained interest in this arena for their ability to limit to replication and transmissibility of arboviruses, through a phenomenon named "superinfection exclusion". While ISVs have great promise as biocontrol agents of arboviruses, research of this phenomenon in vivo is still in its infancy. This project aims to establish a generationally persistent ISV infection in a key Australian arbovirus vector mosquito species. The student will assist with rearing, testing and selective breeding of experimentally infected mosquitoes. This foundational work will allow for future experiments on ISV invasion into naive mosquito populations and in vivo superinfection exclusion tests to be performed. |
Expected outcomes and deliverables | The student will develop general molecular biology skills such as RNA extraction, RT-qPCR and gel electrophoresis. They will also gain experience and knowledge in the rearing and lifecycle of mosquitoes. Dissections of mosquitoes and preparation of specimens for immunofluorescence assay microscopy may also be performed. |
Suitable for | General familiarity with wet laboratory skills such as pipetting is desirable, though training will be provided for specific techniques. |
Primary Supervisor | Associate Professor Leon Hugo Primary contact: Hayden Rohlf |
Instructions to applicants | The supervisor MUST be contacted by students prior to submission of an application. |
Improving bone marrow/stem cell transplant outcomes through pre-transplant modulation of donor T cell function.
Hours of engagement | 36 |
Location | Herston: QIMR Berghofer |
Project description | Background & Hypothesis: Donor stem cell/bone marrow transplantation (allo-SCT/BMT) is an important curative therapy in the treatment of blood cancers, however its application is limited by serious complications such as graft-versus-host disease (GVHD) that have a significant impact on patient mortality and quality of life. Early inflammatory responses during preparative transplant conditioning initiate a cascade of adaptive immune responses that manifest as acute and/or chronic tissue damage in >50% of transplant recipients. GVHD treatment options are relatively limited and focused on immunosuppression and steroidal therapy, which are problematic due to opportunistic infection and refractory disease, therefore new therapies are urgently needed. Donor-derived T cells are known to be the key drivers of GVHD pathology but are also critical to maintain ongoing anti-tumour immunity, also known as Graft-versus-leukaemia (GVL) effects, which prevent cancer relapse in these patients. Identifying novel ways to target GVHD whilst maintaining GVL is key to improving patient outcomes. We propose that in vivo screening of potential therapeutic targets via manipulation of donor T cells pre-transplant will accelerate therapeutic development in this area. Aims & Approach: In this study, we will utilise recent advances in CRISPR-mediated gene therapy to modulate T cell function in naïve primary T cells for allo-SCT. This will involve optimisation, testing and validation of CRISPR gene editing of novel targets in naïve mouse T cells in vitro prior to transplant into allogeneic mice. |
Expected outcomes and deliverables | Students will develop new skills in techniques relevant to immunology research such as immune cell isolation, gene modification and exposure to in vivo models of inflammatory disease. This is an ideal opportunity to gain experience in the laboratory and will aid in future career choices (e.g. Honours, PhD & beyond). Students may be expected to produce either a report or poster and a short oral presentation at the end of their project. |
Suitable for | We are looking for students with a strong interest in immunology who are keen to learn new techniques relevant to the field, e.g. flow cytometry, immune cell isolation, in vitro cell culture etc. |
Primary Supervisor | Associate Professor Kate Gartlan Kate.Gartlan@qimrberghofer.edu.au Immunopathology Laboratory, Infection and Inflammation Program, QIMRB |
Instructions to applicants | The supervisor MUST be contacted by students prior to submission of an application. |
Investigating therapy resistance in ovarian cancer
Hours of engagement | 24 (9am-3 pm Mon, Tues, Thurs, Fri) |
Location | Herston: QIMR Berghofer |
Project description | High grade serous ovarian cancer is the most common type of ovarian cancer and is usually diagnosed at an advanced stage. Platinum based chemotherapy and PARP inhibitors can be very effective treatments, however many patients rapidly become resistant. This project will contribute to a larger investigation into mechanisms of treatment resistance and the identification of treatments to reduce the development of resistance. We have recently developed a set of cell lines with acquired resistance to three ovarian cancer treatments. As part of our ongoing effort to characterise these cell lines this project aims to measure changes to cell growth and response to treatment in the resistance cells. This work will determine the degree of treatment resistance in each line and assist in uncovering the mechanism of resistance. |
Expected outcomes and deliverables | Techniques learnt will include mammalian tissue culture, microplate assays, time-lapse microscopy, dose-response assays and data analysis. Students will be asked to summarise and present their results at a lab meeting. |
Suitable for | Basic laboratory skills, prior experience with aseptic technique and/of cell culture will be an advantage. |
| Additional requirements | This project requires Hepatitis B vaccination (immunisation history statement or certificate). |
Primary Supervisor | Dr Jacinta Simmons |
Instructions to applicants | The supervisor MUST be contacted by students prior to submission of an application. |
Multimodal AI copilot for spatial transcriptomics in melanoma pathology
Hours of engagement | 36 |
Location | Herston: QIMR Berghofer |
Project description | The integration of deep learning and spatial transcriptomics is revolutionising digital pathology by enhancing the accuracy of cancer diagnosis and prognosis. Current AI-powered pathology assistants, such as multimodal large language models (LLMs), have demonstrated the ability to interpret histopathological images and provide clinical insights. However, these models primarily rely on morphological features alone, without leveraging spatially resolved gene expression data. To address this limitation, this project aims to develop an AI copilot for spatial transcriptomics-based melanoma diagnosis, providing molecularly guided AI predictions to assist pathologists in identifying high-risk regions and making more accurate clinical decisions. Aim & Hypothesis: We hypothesise that integrating spatial transcriptomics with histopathology images using contrastive learning and LLMs will significantly improve cancer diagnosis accuracy compared to traditional pathology-based AI approaches. Specifically, by learning multimodal representations, the AI copilot can infer cell types, marker gene expression, and tumour microenvironment (TME), leading to more precise and explainable melanoma diagnostics. |
Expected outcomes and deliverables | Applicants will gain hands-on experience in processing spatial transcriptomics datasets, preprocessing and aligning H&E histopathology images, and applying advanced deep learning techniques such as contrastive learning and multimodal models for gene expression prediction. They will fine-tune large language models (LLMs) for clinical pathology queries and develop a user-friendly AI-powered web interface for real-time pathology assistance. Working closely with pathologists, applicants will gain insights into real-world challenges in melanoma diagnosis while contributing to cutting-edge AI-driven biomedical research. |
Suitable for | Applicants should have a background in computer science, bioinformatics, or biomedical engineering, with skills in Python programming and machine learning. Experience with deep learning frameworks (eg. PyTorch) and basic data analysis is important. Knowledge of digital pathology or spatial transcriptomics is a plus but not required. The ideal candidate is curious, eager to learn, and able to work independently while collaborating with a team of researchers and pathologists. |
Primary Supervisor | Associate Professor Quan Nguyen Primary contact: Xiao.Tan@qimrb.edu.au |
Instructions to applicants | The supervisor MUST be contacted by students prior to submission of an application. |