![Dr Quan Nguyen](/sites/default/files/profiles/16084.jpeg)
Overview
Background
Dr Quan Nguyen is a Group Leader at the Institute for Molecular Bioscience (IMB), The University of Queensland. He is leading the Genomics and Machine Learning (GML) lab to study neuroinflammation and cancer-immune cells at single-cell resolution and within spatial morphological tissue context. His research interest is about revealing gene and cell regulators that determine the states of the complex cancer and neuronal ecosystems. Particularly, he is interested in quantifying cellular diversity and the dynamics of cell-cell interactions within the tissues to find ways to improve cancer diagnosis or cell-type specific treatments or the immunoinflammation responses that cause neuronal disease.
Using machine learning and genomic approaches, his group are integrating single-cell spatiotemporal sequencing data with tissue imaging data to find causal links between cellular genotypes, tissue microenvironment, and disease phenotypes. GML lab is also developing experimental technologies that enable large-scale profiling of spatial gene and protein expression (spatial omics) in a range of cancer tissues (focusing on brain and skin cancer) and in mouse brain and spinal cord.
Dr Quan Nguyen completed a PhD in Bioengineering at the University of Queensland in 2013, postdoctoral training in Bioinformatics at RIKEN institute in Japan in 2015, a CSIRO Office of Chief Executive (OCE) Research Fellowship in 2016, an IMB Fellow in 2018, an Australian Research Council DECRA fellowship (2019-2021), and is currently a National Health and Medical Research Council leadership fellow (EL2). He has published in top-tier journals, including Cell, Cell Stem Cell, Nature Methods, Nature Protocols, Nature Communications, Genome Research, Genome Biology and a prize-winning paper in GigaScience. In the past three years, he has contributed to the development of x8 open-source software, x2 web applications, and x4 databases for analysis of single-cell data and spatial transcriptomics. He is looking for enthusiastic research students and research staff to join his group.
Availability
- Dr Quan Nguyen is:
- Available for supervision
- Media expert
Fields of research
Qualifications
- Doctor of Philosophy, The University of Queensland
Research interests
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Biomedical Machine Learning
His research focusses on integrating single cell spatiotemporal data with large-scale population genomics data to find causal relationship between DNA variants, gene expression and diseases. Using machine learning approaches to analyse multidimensional sequencing and imaging data, he computationally reconstructs biological regulatory networks between genes in a cell and cells within a tissue. The systematic understanding of regulatory networks and biomarkers that are specific to individuals and cell types will contribute to early disease diagnosis, targeted drug discovery and precision medicine.
Research impacts
Genomics research for the past decade has relied on data from bulk sequencing of dissociated tissues. The problem with this approach is it discards both intercellular variation among cancer cells and spatial information within a tumour. Dr Nguyen's Cancer Spatial Omics (CSO) program applied spatial omics and machine learning to contextualise cellular genomics landscape within tumour biopsies and across patients. CSO's reach is well entrenched within national and international clinical collaborations where it is already having clinical impact by improving cancer histological diagnosis, and it is empowering a wide field of researchers and clinicians.
His CSO program has advanced understandings of cellular ecosystems in health and disease:
- resolved intra- and inter-patient heterogeneity (Genome Biol, 2019 & 2021)
- spatially maped cellular microenvironment (Cell, 2020; bioRxiv125658v1, 2020; J Immunother Cancer, 2020)
- discovered gene (dys)regulations underlying cell differentiation and proliferation (Cell Stem Cell, 2018; Nat communs 2017, 2017, 2021)
- found new cell types (Genome Res, 2018; EMBO journal, 2019; Genome Biol, 2021)
- transformed digital pathology diagnosis applications (Bioinformatics, 2020; Artificial Neural Networks, 2020; bioRxiv436004; bioRxiv125658v1)
- produced software to enhance analysis capability (GigaScience, 2018; Genome Biol 2019 & 2019; Cell Systems, 2020; Bioinformatics, 2020; bioRxiv125658v1)
- developed new genomics technologies (Nat Prot, 2018; Cell, 2020; Genome Biol, 2021).
Works
Search Professor Quan Nguyen’s works on UQ eSpace
2024
Journal Article
The activity of early-life gene regulatory elements is hijacked in aging through pervasive AP-1-linked chromatin opening
Patrick, Ralph, Naval-Sanchez, Marina, Deshpande, Nikita, Huang, Yifei, Zhang, Jingyu, Chen, Xiaoli, Yang, Ying, Tiwari, Kanupriya, Esmaeili, Mohammadhossein, Tran, Minh, Mohamed, Amin R., Wang, Binxu, Xia, Di, Ma, Jun, Bayliss, Jacqueline, Wong, Kahlia, Hun, Michael L., Sun, Xuan, Cao, Benjamin, Cottle, Denny L., Catterall, Tara, Barzilai-Tutsch, Hila, Troskie, Robin-Lee, Chen, Zhian, Wise, Andrea F., Saini, Sheetal, Soe, Ye Mon, Kumari, Snehlata, Sweet, Matthew J. ... Nefzger, Christian M. (2024). The activity of early-life gene regulatory elements is hijacked in aging through pervasive AP-1-linked chromatin opening. Cell Metabolism, 36 (8), 1858-1881.e23. doi: 10.1016/j.cmet.2024.06.006
2024
Conference Publication
Spatial Transcriptomic Sequencing Of A DIPG Patient Tumour Resource
Kordowski, Anja, Mulay, Onkar, Harris, Lachlan, Nguyen, Quan H and Day, Bryan W. (2024). Spatial Transcriptomic Sequencing Of A DIPG Patient Tumour Resource. 2024 International Symposium on Pediatric Neuro-Oncology (ISPNO), Philadelphia, PA United States, 29 June – 2 July 2024. Cary, NC United States: Oxford University Press. doi: 10.1093/neuonc/noae064.102
2024
Journal Article
Exercise rejuvenates microglia and reverses T cell accumulation in the aged female mouse brain
Chauquet, Solal, Willis, Emily F., Grice, Laura, Harley, Samuel B. R., Powell, Joseph E., Wray, Naomi R., Nguyen, Quan, Ruitenberg, Marc J., Shah, Sonia and Vukovic, Jana (2024). Exercise rejuvenates microglia and reverses T cell accumulation in the aged female mouse brain. Aging Cell, 23 (7) e14172, e14172. doi: 10.1111/acel.14172
2024
Conference Publication
A spatially mapped gene expression signature for intestinal stem-like cells identifies high-risk precursors of gastric cancer
Wichmann, Ignacio A., Huang, Robert J., Su, Andrew, Sathe, Anuja, Shum, Miranda V., Grimes, Susan M., Meka, Rithika, Almeda, Alison, Bai, Xiangqi, Shen, Jeanne, Nguyen, Quan, Amieva, Manuel, Hwang, Joo Ha and Ji, Hanlee P. (2024). A spatially mapped gene expression signature for intestinal stem-like cells identifies high-risk precursors of gastric cancer. American Association for Cancer Research Annual Meeting 2024, San Diego, CA United States, 5-10 April 2024. Philadelphia, PA United States: American Association for Cancer Research. doi: 10.1158/1538-7445.am2024-1140
2024
Journal Article
Single-cell RNA sequencing reveals peripheral blood leukocyte responses to spinal cord injury in mice with humanised immune systems
Gillespie, Ellen R., Grice, Laura F., Courtney, Isabel G., Lao, Hong Wa, Jung, Woncheol, Ramkomuth, Sonny, Xie, Jacky, Brown, David A., Walsham, James, Radford, Kristen J., Nguyen, Quan H. and Ruitenberg, Marc J. (2024). Single-cell RNA sequencing reveals peripheral blood leukocyte responses to spinal cord injury in mice with humanised immune systems. Journal of Neuroinflammation, 21 (1) 63, 1-20. doi: 10.1186/s12974-024-03048-0
2024
Journal Article
Genome-wide transcriptome analysis reveals the diversity and function of long non-coding RNAs in dinoflagellates
Chen, Yibi, Dougan, Katherine E., Nguyen, Quan, Bhattacharya, Debashish and Chan, Cheong Xin (2024). Genome-wide transcriptome analysis reveals the diversity and function of long non-coding RNAs in dinoflagellates. NAR Genomics and Bioinformatics, 6 (1) lqae016, lqae016. doi: 10.1093/nargab/lqae016
2024
Journal Article
Cellular neighborhood analysis in spatial omics reveals new tissue domains and cell subtypes
Ruitenberg, Marc J. and Nguyen, Quan H. (2024). Cellular neighborhood analysis in spatial omics reveals new tissue domains and cell subtypes. Nature Genetics, 56 (3), 362-364. doi: 10.1038/s41588-023-01646-x
2024
Other Outputs
DeepHis2Exp
Jiang, Yuanhao, Nguyen, Quan and Tan, Xiao (2024). DeepHis2Exp. The University of Queensland. (Dataset) doi: 10.48610/3e7a235
2024
Journal Article
Challenges and perspectives in computational deconvolution of genomics data
Garmire, Lana X., Li, Yijun, Huang, Qianhui, Xu, Chuan, Teichmann, Sarah A., Kaminski, Naftali, Pellegrini, Matteo, Nguyen, Quan and Teschendorff, Andrew E. (2024). Challenges and perspectives in computational deconvolution of genomics data. Nature Methods, 21 (3), 1-10. doi: 10.1038/s41592-023-02166-6
2024
Journal Article
Cellular heterogeneity of pluripotent stem cell-derived cardiomyocyte grafts is mechanistically linked to treatable arrhythmias
Selvakumar, Dinesh, Clayton, Zoe E., Prowse, Andrew, Dingwall, Steve, Kim, Sul Ki, Reyes, Leila, George, Jacob, Shah, Haisam, Chen, Siqi, Leung, Halina H. L., Hume, Robert D., Tjahjadi, Laurentius, Igoor, Sindhu, Skelton, Rhys J. P., Hing, Alfred, Paterson, Hugh, Foster, Sheryl L., Pearson, Lachlan, Wilkie, Emma, Marcus, Alan D., Jeyaprakash, Prajith, Wu, Zhixuan, Chiu, Han Shen, Ongtengco, Cherica Felize J., Mulay, Onkar, McArthur, Jeffrey R., Barry, Tony, Lu, Juntang, Tran, Vu ... Chong, James J. H. (2024). Cellular heterogeneity of pluripotent stem cell-derived cardiomyocyte grafts is mechanistically linked to treatable arrhythmias. Nature Cardiovascular Research, 3 (2), 145-165. doi: 10.1038/s44161-023-00419-3
2024
Journal Article
Clinicopathologic characteristics of thyroid microcarcinoma: findings from a hospital-based study in Vietnam
Nguyen, Bay Quang, Vu, Hai Thi, Nguyen, Linh My Thi, Nguyen, Quan H., Paragomi, Pedram and Luu, Hung N. (2024). Clinicopathologic characteristics of thyroid microcarcinoma: findings from a hospital-based study in Vietnam. Discovery Medicine, 36 (180), 209-216. doi: 10.24976/discov.med.202436180.20
2023
Journal Article
HOPX-associated molecular programs control cardiomyocyte cell states underpinning cardiac structure and function
Friedman, Clayton E., Cheetham, Seth W., Negi, Sumedha, Mills, Richard J., Ogawa, Masahito, Redd, Meredith A., Chiu, Han Sheng, Shen, Sophie, Sun, Yuliangzi, Mizikovsky, Dalia, Bouveret, Romaric, Chen, Xiaoli, Voges, Holly K., Paterson, Scott, De Angelis, Jessica E., Andersen, Stacey B., Cao, Yuanzhao, Wu, Yang, Jafrani, Yohaann M.A., Yoon, Sohye, Faulkner, Geoffrey J., Smith, Kelly A., Porrello, Enzo, Harvey, Richard P., Hogan, Benjamin M., Nguyen, Quan, Zeng, Jian, Kikuchi, Kazu, Hudson, James E. and Palpant, Nathan J. (2023). HOPX-associated molecular programs control cardiomyocyte cell states underpinning cardiac structure and function. Developmental Cell, 59 (1), 91-107.e1. doi: 10.1016/j.devcel.2023.11.012
2023
Journal Article
Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues
Pham, Duy, Tan, Xiao, Balderson, Brad, Xu, Jun, Grice, Laura F., Yoon, Sohye, Willis, Emily F., Tran, Minh, Lam, Pui Yeng, Raghubar, Arti, Kalita-de Croft, Priyakshi, Lakhani, Sunil, Vukovic, Jana, Ruitenberg, Marc J. and Nguyen, Quan H. (2023). Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues. Nature Communications, 14 (1) 7739, 1-25. doi: 10.1038/s41467-023-43120-6
2023
Journal Article
Deep spatial-omics analysis of Head & Neck carcinomas provides alternative therapeutic targets and rationale for treatment failure
Causer, Andrew, Tan, Xiao, Lu, Xuehan, Moseley, Philip, Teoh, Siok M., Molotkov, Natalie, McGrath, Margaret, Kim, Taehyun, Simpson, Peter T, Perry, Christopher, Frazer, Ian H., Panizza, Benedict, Ladwa, Rahul, Nguyen, Quan and Gonzalez-Cruz, Jazmina L. (2023). Deep spatial-omics analysis of Head & Neck carcinomas provides alternative therapeutic targets and rationale for treatment failure. n p j Precision Oncology, 7 (1) 89, 89. doi: 10.1038/s41698-023-00444-2
2023
Journal Article
Tolerogenic dendritic cells protect against acute kidney injury
Li, Jennifer S.Y., Robertson, Harry, Trinh, Katie, Raghubar, Arti M., Nguyen, Quan, Matigian, Nicholas, Patrick, Ellis, Thomson, Angus W., Mallett, Andrew J. and Rogers, Natasha M. (2023). Tolerogenic dendritic cells protect against acute kidney injury. Kidney International, 104 (3), 492-507. doi: 10.1016/j.kint.2023.05.008
2023
Journal Article
A robust platform for integrative spatial multi‐omics analysis to map immune responses to SARS‐CoV‐2 infection in lung tissues
Tan, Xiao, Grice, Laura F., Tran, Minh, Mulay, Onkar, Monkman, James, Blick, Tony, Vo, Tuan, Almeida, Ana Clara, da Silva Motta, Jarbas, Fernandes de Moura, Karen, Machado‐Souza, Cleber, Souza‐Fonseca‐Guimaraes, Paulo, Baena, Cristina Pellegrino, de Noronha, Lucia, Guimaraes, Fernanda Simoes Fortes, Luu, Hung N., Drennon, Tingsheng, Williams, Stephen, Stern, Jacob, Uytingco, Cedric, Pan, Liuliu, Nam, Andy, Cooper, Caroline, Short, Kirsty, Belz, Gabrielle T., Souza‐Fonseca‐Guimaraes, Fernando, Kulasinghe, Arutha and Nguyen, Quan (2023). A robust platform for integrative spatial multi‐omics analysis to map immune responses to SARS‐CoV‐2 infection in lung tissues. Immunology, 170 (3), 401-418. doi: 10.1111/imm.13679
2023
Journal Article
Global analysis of transcription start sites and enhancers in endometrial stromal cells and differences associated with endometriosis
Marla, Sushma, Mortlock, Sally, Yoon, Sohye, Crawford, Joanna, Andersen, Stacey, Mueller, Michael D., McKinnon, Brett, Nguyen, Quan and Montgomery, Grant W. (2023). Global analysis of transcription start sites and enhancers in endometrial stromal cells and differences associated with endometriosis. Cells, 12 (13) 1736, 1-22. doi: 10.3390/cells12131736
2023
Journal Article
In vivo inhibition of nuclear ACE2 translocation protects against SARS-CoV-2 replication and lung damage through epigenetic imprinting
Tu, Wen Juan, Melino, Michelle, Dunn, Jenny, McCuaig, Robert D., Bielefeldt-Ohmann, Helle, Tsimbalyuk, Sofiya, Forwood, Jade K., Ahuja, Taniya, Vandermeide, John, Tan, Xiao, Tran, Minh, Nguyen, Quan, Zhang, Liang, Nam, Andy, Pan, Liuliu, Liang, Yan, Smith, Corey, Lineburg, Katie, Nguyen, Tam H., Sng, Julian D. J., Tong, Zhen Wei Marcus, Chew, Keng Yih, Short, Kirsty R., Le Grand, Roger, Seddiki, Nabila and Rao, Sudha (2023). In vivo inhibition of nuclear ACE2 translocation protects against SARS-CoV-2 replication and lung damage through epigenetic imprinting. Nature Communications, 14 (1) 3680, 3680. doi: 10.1038/s41467-023-39341-4
2023
Other Outputs
Deep spatial-omics analysis of head & neck carcinomas provides alternative therapeutic targets and rational for treatment failure
Andrew Causer, Xiao Tan, Xuehan Lu, Philip Moseley, Min Teoh, Natalie Molotkov, Margaret McGrath, Taehyun Kim, Peter Simpson, Christopher Perry, Ian Frazer, Benedict Panizza, Rahul Ladwa, Quan Nguyen and Jazmina L Gonzalez-Cruz (2023). Deep spatial-omics analysis of head & neck carcinomas provides alternative therapeutic targets and rational for treatment failure. The University of Queensland. (Dataset) doi: 10.48610/698bb9e
2023
Journal Article
Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity
Sun, Yuliangzi, Shim, Woo Jun, Shen, Sophie, Sinniah, Enakshi, Pham, Duy, Su, Zezhuo, Mizikovsky, Dalia, White, Melanie D., Ho, Joshua W. K., Nguyen, Quan, Bodén, Mikael and Palpant, Nathan J (2023). Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity. Nucleic Acids Research, 51 (11), e62-e62. doi: 10.1093/nar/gkad307
Funding
Current funding
Supervision
Availability
- Dr Quan Nguyen is:
- Available for supervision
Before you email them, read our advice on how to contact a supervisor.
Available projects
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Analysis of Spatial Data (Multiple student projects)
Nguyen group’s research is focused on understanding cancer complexity at tissue level by applying single-cell sequencing, spatial transcriptomics and tissue imaging, statistical learning and deep learning, and high performance computing. Most molecular biological data are from dissociated cells, which were separated from their original tissues, and thus the spatial connectin information is missing. Furthermore, these data often represent average measurements of millions of cells, which mask subtle differences that are specific for individual cells. From sequencing and imaging data, the group aims to computationally reconstruct biological regulatory networks underlying human diseases in every single cell within an indissociated tissue, like a tumour. The group develops both experimental and analytical methods to integrate genomics and imaging data for earlier and more accurate diagnosis and prognosis of diseases in tissue biopsies. Particularly, the group focuses on cancer (brain and skin cancer) and neuronal inflammation responses. Through advancing the understanding of biomarkers and cellular regulatory networks that are specific to individuals and cell types, the group contributes to early disease diagnosis, targeted drug discovery and precision medicine.
Traineeships, honours and PhD projects include
- Analyse spatial transcriptomics data of brain and skin cancer tissue to find cell-cell interactions, cell-type specific responses and cancer microenvironment evolution
- Develop experimental approaches to study spatial transcriptomics of human cancer cells in brain cancer xenograft models
- Develop experimental approaches to study formalin-fixed tissue sections for human skin cancer tissue sections
- Develop analysis methods to combine sequencing and imaging data from spatial transcriptomics experiments of skin cancer tissue sections
- Develop analysis methods to combine spatial transcriptomics, immuno-fluorescence images and histopathological images
- Find single cell gene regulatory networks in healthy and diseased cells from single cell and spatial datasets of human skin cancer samples
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Regulatory Networks Determining Cell Types and Cell States
This project aims to use single-cell gene regulation networks to predict cell types and cell states in healthy and diseased tissues.
Through cell differentiation and division, a single fertilised egg gives rise to ~37.2 trillion cells with remarkable variation in forms and functions to make up the human body. A long-sought research goal over the past 150 years is to understand cell types and their properties and how they affect health and respond to environments. Conventional methods to assess cell type variability often rely on a small number of pre-characterised biomarkers and use population average measurements of millions of cells per sample, which is limited in resolution, accuracy, sensitivity, specificity, and comprehensiveness. Diverse cellular phenotypes encoded by the same genome are results from the differential regulation of large gene expression networks with about 22,000 genes. ‘Cell type’ and ‘cell state’ represent persistent and transient cellular properties, which can be defined by data-driven, network-based approaches. A systems-biology approach, which utilises advances in the computational analysis of big biological data and single-cell technologies, can be the key to decode the biological program in every cell type in the human body, thereby leading to better understanding and control of organismal phenotypes at the single-cell level.
The international Human Cell Atlas consortium (HCA) will release the first draft atlas comprising ~30-100 million cells for 15 organ systems in 1-2 years. Although at least 10 billion cells representing all tissues will be generated for the complete Atlas (Regev et al., 2017), the number is still marginal, accounting for 0.02% of the total 37 trillion cells in the body. Therefore, computational approaches are needed to recapitulate how the cells program the shared genome sequence in a human body to create astoundingly diverse forms and functions. From quantitative measurements of thousands of genes expressed in every cell, it is possible to reconstruct gene regulatory networks (GRN), the cellular programs. Regulatory ‘rules/patterns’ for molecular interactions are universally applicable in both population and single-cell data, and thus can be used to integrate datasets at single-cell and bulk-sample levels to infer GRN. This project will use gene expression regulatory networks to systematically quantify differences between cell types and cell states at single-cell resolution based. We will apply established analysis methods as well as develop new algorithms and software to integrate high-resolution scRNA-Seq data with large-scale population transcriptomics, genetics and epigenetics data to reconstruct gene regulatory networks. The ultimate aim is to predict the cell type and cell state of an unknown cell, by comparing the cell’s gene expression values to the largest single-cell regulatory network database generated in this project. The research would enable to predict cellular programs for thousands of cell types, which should contribute to the unprecedented ability to control and reprogram cells, to detect aberrant cells, and to understand how cells respond to the environment. Particularly, this project will contribute to studying cancer cell types and cell states at single-cell levels.
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Spatial omics and machine learning to study heterogeneity and interaction between cells in primary tissues
This project aims at studying cell-cell and gene-gene regulatory networks in primary tissues by deep machine learning analysis of population, single-cell and spatial omics data.
Advances in genomics technologies enable data generation at an unprecedented speed, both in scale (hundreds of thousands of samples) and resolution (single cell). Machine learning in human genomics is an emerging field, which uses the power of statistics and high-performance computers in combination with biological knowledge to extract new information relevant to disease diagnosis and treatment.
Personalised and precision medicine require system genomics research to resolve variability at the cell, tissue and inter-individual level (e.g. different genetic background, age, exposure to environment). While big data integration of population genetics and single-cell omics studies can address variability between isolated cells and between individuals, a particularly important information dimension that is currently lacking is the heterogeneity in cell type composition and cell-cell interaction within the physiological context of a tissue. Such information is lost due to cell dissociation, a requirement for almost all molecular genomics assays.
We will contribute to research in personalised and precision medicine through deciphering the complex heterogeneity between cell types, tissues, and individuals by comprehensively integrating single-cell and population genetics with spatial transcriptomics, a novel type of information that is just beginning to be measured at a genome scale. Traditional machine learning and recent deep learning approaches for integrating multimodal genomics datatypes from bulk and single cells and image data will be applied. The systematic understanding of regulatory networks and biomarkers in a physiological context, which is specific to individuals and cell types will contribute to early disease diagnosis, targeted drug discovery and precision medicine. The research will generate an important understanding of variation in molecular networks inside individual cells and among neighbouring cells in specific microenvironments and among distant cell types involved in multi-organ communication, all of which underlie causal relationships between genotype and phenotype. The student will enjoy a conducive learning and research environment to develop a unique combination of multidisciplinary expertise in experimental biology, systems biology, biostatistics, and bioinformatics, and artificial intelligence.
Supervision history
Current supervision
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Doctor Philosophy
Deep learning analysis of spatial-omics and histopathological images to predict prognosis in gastrointestinal cancer
Principal Advisor
Other advisors: Dr Mitchell Stark
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Doctor Philosophy
Development of Multimodal Machine Learning Tools for Precision Pulmonary Fibrosis Care
Principal Advisor
Other advisors: Professor Dan Chambers
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Doctor Philosophy
Discovering novel cell-type specific non-coding RNA and dissecting their role in cancer
Principal Advisor
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Doctor Philosophy
Spatial transcriptomics and Single-cell Interaction Analysis
Principal Advisor
Other advisors: Dr Laura Grice, Dr Laura Genovesi
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Doctor Philosophy
Single Cell Multiomics for Precision Medicine in Cancer
Principal Advisor
Other advisors: Dr Jazmina Gonzalez Cruz
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Doctor Philosophy
Translational meaning of the efficacy of immunotherapies as neoadjuvants to treat Head and Neck cancers.
Associate Advisor
Other advisors: Dr Kelvin Tuong, Associate Professor James Wells, Dr Yang Yang, Dr Jazmina Gonzalez Cruz
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Doctor Philosophy
Structural and cellular analysis of Rab GTPases for drug development in cancer.
Associate Advisor
Other advisors: Professor Jennifer Stow, Professor Brett Collins
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Doctor Philosophy
Deciphering the age-altered transcription factor network and its control over regenerative potential
Associate Advisor
Other advisors: Dr Christian Nefzger
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Doctor Philosophy
Circular RNAs as a novel biomarker for ovarian cancer
Associate Advisor
Other advisors: Associate Professor Jason Lee
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Doctor Philosophy
predicting melanoma survival using advanced digital imaging technologies
Associate Advisor
Other advisors: Professor Kiarash Khosrotehrani
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Doctor Philosophy
Understanding the tumour microenvironment of head and neck squamous cell carcinoma
Associate Advisor
Other advisors: Dr Arutha Kulasinghe
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Doctor Philosophy
Defining the critical determinants of viral-mediated pneumonitis after bone marrow transplantation.
Associate Advisor
Other advisors: Dr Seweryn Bialasiewicz, Dr Antiopi Varelias
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Doctor Philosophy
Harnessing the power of spatial "omics" to develop innovative approaches for spinal cord repair
Associate Advisor
Other advisors: Dr Laura Grice, Professor Marc Ruitenberg
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Doctor Philosophy
The human pulmonary fibrosis transcriptome at single cell resolution
Associate Advisor
Other advisors: Professor Dan Chambers
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Doctor Philosophy
Using signatures of cell identity to improve cell type prediction in single cell analysis pipelines
Associate Advisor
Other advisors: Dr Woo Jun Shim, Associate Professor Nathan Palpant
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Doctor Philosophy
Using statistical genetics approaches to gain insight into patterns of variation in complex traits
Associate Advisor
Other advisors: Professor Geoffrey McLachlan, Dr Vivi Arief, Emeritus Professor Kaye Basford
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Doctor Philosophy
Cellular genomics of Parkinson's disease
Associate Advisor
Other advisors: Dr Allan McRae, Honorary Professor Jake Gratten, Dr Yuanhao Yang
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Doctor Philosophy
Precision diagnostics for early melanoma detection
Associate Advisor
Other advisors: Professor Peter Soyer, Dr Snehlata Kumari, Dr Mitchell Stark
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Doctor Philosophy
Multilineage differentiation from pluripotency reveals genetic regulators of cardiovascular physiology
Associate Advisor
Other advisors: Associate Professor Nathan Palpant
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Doctor Philosophy
Proteomics and transcriptomics analysis of phenotypic heterogeneity in 3D melanoma spheroids
Associate Advisor
Other advisors: Associate Professor Helmut Schaider, Professor Nikolas Haass
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Doctor Philosophy
The genomic architecture of suspicious lesions and skin in photodamaged and non-photodamaged areas (PhotoMelanoma)
Associate Advisor
Other advisors: Professor Peter Soyer, Dr Brigid Betz-Stablein, Dr Mitchell Stark
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Doctor Philosophy
Deciphering the spatio-temporal landscape of cell-autonomous and non-cell-autonomous drivers of motor neuron death in MND
Associate Advisor
Other advisors: Associate Professor Frederik Steyn, Associate Professor Shyuan Ngo
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Doctor Philosophy
Interpretable AI-Theory and Practice
Associate Advisor
Other advisors: Professor Fred Roosta
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Doctor Philosophy
Effect of hexarelin, a synthetic small peptide, on metabolic balance of obese and diabetic mice
Associate Advisor
Other advisors: Professor Ian Frazer, Associate Professor James Wells, Dr Jazmina Gonzalez Cruz
Completed supervision
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2024
Doctor Philosophy
Machine learning integration of imaging data with spatial multi-omics data to study heterogeneity in disease tissues
Principal Advisor
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2024
Doctor Philosophy
Imaging and Sequencing Analysis of Cellular Regulation and Communication within Spatial Context in Cancer Tissue
Principal Advisor
Other advisors: Dr Christian Nefzger
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2024
Doctor Philosophy
Applying Spatial Multi-omics to Identify Cells and Regulators Driving Cancer Progression and Drug Response
Principal Advisor
Other advisors: Dr Laura Genovesi
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2023
Doctor Philosophy
Developing novel spatio-temporal inference methods using single cell, spatial omics and imaging data for studying neuroinflammation and cancer progression
Principal Advisor
Other advisors: Associate Professor Cheong Xin Chan, Professor Marc Ruitenberg
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2022
Doctor Philosophy
Understanding genetic control of gene expression and its role in disease at a single cell resolution
Principal Advisor
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2024
Doctor Philosophy
Using Transcriptomics technologies in health-related research: Applications and Challenges
Associate Advisor
Other advisors: Dr Sonia Shah
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2024
Doctor Philosophy
Assessing complex gene and genome features of dinoflagellates
Associate Advisor
Other advisors: Associate Professor Cheong Xin Chan
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2023
Doctor Philosophy
Defining the cellular milieu within the clear cell renal cell carcinoma microenvironment
Associate Advisor
Other advisors: Dr Andrew Kassianos, Dr Andrew Mallett
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2023
Doctor Philosophy
Evolution and adaptation of non-symbiotic Symbiodiniaceae
Associate Advisor
Other advisors: Associate Professor Cheong Xin Chan
Media
Enquiries
Contact Dr Quan Nguyen directly for media enquiries about:
- ad cancer treatment
- cancer
- cancer diagnosis
- cancer prognosis
- gene regulatory networks
- genomics
- precision medicine
- single cell
- tissue biology
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