Dr Quan Nguyen

NHMRC Emerging Leadership Fellow

Institute for Molecular Bioscience

Affiliate Senior Research Fellow

School of Biomedical Sciences
Faculty of Medicine
quan.nguyen@imb.uq.edu.au
+61 7 334 62394

Overview

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 or neuronal ecosystem. 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 at RIKEN institute in Japan in 2015, a CSIRO (OCE) Research Fellowship in 2016, an IMB Fellow in 2018, and is currently an ARC DECRA research fellow. 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.

Research Interests

  • 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).

Qualifications

  • Doctor of Philosophy, The University of Queensland

Publications

  • Su, Andrew, Lee, HoJoon, Tan, Xiao, Suarez, Carlos J., Andor, Noemi, Nguyen, Quan and Ji, Hanlee P. (2022). A deep learning model for molecular label transfer that enables cancer cell identification from histopathology images. npj Precision Oncology, 6 (1) 14, 14. doi: 10.1038/s41698-022-00252-0

  • Tuong, Zewen Kelvin, Loudon, Kevin W., Berry, Brendan, Richoz, Nathan, Jones, Julia, Tan, Xiao, Nguyen, Quan, George, Anne, Hori, Satoshi, Field, Sarah, Lynch, Andy G., Kania, Katarzyna, Coupland, Paul, Babbage, Anne, Grenfell, Richard, Barrett, Tristan, Warren, Anne Y., Gnanapragasam, Vincent, Massie, Charlie and Clatworthy, Menna R. (2021). Resolving the immune landscape of human prostate at a single-cell level in health and cancer. Cell Reports, 37 (12) 110132, 110132. doi: 10.1016/j.celrep.2021.110132

  • Grapotte, Mathys, Saraswat, Manu, Bessière, Chloé, Menichelli, Christophe, Ramilowski, Jordan A., Severin, Jessica, Hayashizaki, Yoshihide, Itoh, Masayoshi, Tagami, Michihira, Murata, Mitsuyoshi, Kojima-Ishiyama, Miki, Noma, Shohei, Noguchi, Shuhei, Kasukawa, Takeya, Hasegawa, Akira, Suzuki, Harukazu, Nishiyori-Sueki, Hiromi, Frith, Martin C., Abugessaisa, Imad, Aitken, Stuart, Aken, Bronwen L., Alam, Intikhab, Alam, Tanvir, Alasiri, Rami, Alhendi, Ahmad M. N., Alinejad-Rokny, Hamid, Alvarez, Mariano J., Andersson, Robin, Arakawa, Takahiro ... Lecellier, Charles-Henri (2021). Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network. Nature Communications, 12 (1) 3297. doi: 10.1038/s41467-021-23143-7

View all Publications

Supervision

  • Doctor Philosophy

  • Doctor Philosophy

  • Doctor Philosophy

View all Supervision

Available 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
  • 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.

  • 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.

View all Available Projects

Publications

Book Chapter

Journal Article

Conference Publication

Other Outputs

Grants (Administered at UQ)

PhD and MPhil Supervision

Current Supervision

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

    Other advisors:

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

    Other advisors:

  • Doctor Philosophy — Principal Advisor

    Other advisors:

  • Master Philosophy — Principal Advisor

    Other advisors:

  • Doctor Philosophy — Principal Advisor

    Other advisors:

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Associate Advisor

  • Doctor Philosophy — Associate Advisor

    Other advisors:

  • Doctor Philosophy — Associate Advisor

  • Doctor Philosophy — Associate Advisor

  • Doctor Philosophy — Associate Advisor

    Other advisors:

  • Doctor Philosophy — Associate Advisor

  • Doctor Philosophy — Associate Advisor

  • Doctor Philosophy — Associate Advisor

    Other advisors:

  • Doctor Philosophy — Associate Advisor

    Other advisors:

  • Doctor Philosophy — Associate Advisor

    Other advisors:

  • Doctor Philosophy — Associate Advisor

  • Doctor Philosophy — Associate Advisor

    Other advisors:

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • 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
  • 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.

  • 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.