Dr Quan Nguyen

Senior Research Officer

Institute for Molecular Bioscience
quan.nguyen@imb.uq.edu.au
+61 7 334 62394

Overview

Dr Quan Nguyen completed a PhD in Bioengineering at the University of Queensland in 2013. From 2013-2015, he was a postdoctoral researcher at RIKEN institute in Japan, where he worked in Prof Piero Carninci’s Division of Genomics Technology. From 2015-2016, he received a CSIRO Office of Chief Executive Fellowship on the development of computational methods to study genomic regulatory elements in mammals. In October 2016, he joined Institute for Molecular Bioscience (IMB) as a senior research officer and was promoted to IMB fellow in 2018. Since 2019, he is leading a Biomedical Machine Learning lab at IMB and is looking for enthusiastic research students and research assistants to join his team.

Research Interests

  • Biomedical Machine Learning
    My 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, I aim to computationally reconstruct biological regulatory networks underlying human diseases. 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

Through utilizing a combination of multidisciplinary expertise in experimental biology, systems biology, biostatistics, and bioinformatics, Dr Nguyen has led successful research projects exploring cutting-edge topics in genomics and transcriptomics, including the analysis of some of the world’s largest genomics data sets. These projects have all resulted in the publication in top tier journals, including x1 Cell Stem Cell, x1 Nature Protocols, x3 Nature Communications, x1 Genome Research and a prize-winning paper in GigaScience. He has produced x5 bioinformatics software tools, x2 web applications, and x4 databases. Through his research, he has identified novel biomarkers, developed new sequencing technologies and computational methods that have implications in both manufacturing and healthcare. He has been successful in receiving ~$2M in funding, including highly competitive fellowships at national and international level.

Qualifications

  • Doctor of Philosophy, The University of Queensland

Publications

  • Friedman, Clayton E., Nguyen, Quan, Lukowski, Samuel W., Helfer, Abbigail, Chiu, Han Sheng, Miklas, Jason, Levy, Shiri, Suo, Shengbao, Han, Jing-Dong Jackie, Osteil, Pierre, Peng, Guangdun, Jing, Naihe, Baillie, Greg J., Senabouth, Anne, Christ, Angelika N., Bruxner, Timothy J., Murry, Charles E., Wong, Emily S., Ding, Jun, Wang, Yuliang, Hudson, James, Ruohola-Baker, Hannele, Bar-Joseph, Ziv, Tam, Patrick P.L., Powell, Joseph E. and Palpant, Nathan J. (2018) Single-cell transcriptomic analysis of cardiac differentiation from human PSCs reveals HOPX-dependent cardiomyocyte maturation. Cell Stem Cell, 23 4: 586-598. doi:10.1016/j.stem.2018.09.009

  • Daniszewski, Maciej, Nguyen, Quan, Chy, Hun S., Singh, Vikrant, Crombie, Duncan E., Kulkarni, Tejal, Liang, Helena H., Sivakumaran, Priyadharshini, Lidgerwood, Grace E., Hernández, Damián, Conquest, Alison, Rooney, Louise A., Chevalier, Sophie, Andersen, Stacey B., Senabouth, Anne, Vickers, James C., Mackey, David A., Craig, Jamie E., Laslett, Andrew L., Hewitt, Alex W., Powell, Joseph E. and Pébay, Alice (2018) Single-cell profiling identifies key pathways expressed by iPSCs cultured in different commercial media. iScience, 7 30-39. doi:10.1016/j.isci.2018.08.016

  • Lukowski, S. W., Tuong, Z. K., Noske, K., Senabouth, A., Nguyen, Q. H., Andersen, S. B., Soyer, H. P., Frazer, I. H. and Powell, J. E. (2018) Detection of HPV E7 transcription at single-cell resolution in epidermis. The Journal of Investigative Dermatology, . doi:10.1016/j.jid.2018.06.169

View all Publications

Available Projects

  • 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 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.

    Personalised and precision medicine require system genomics research to resolve variability at the cell, tissue and the organismal/inter-individual level (e.g. genetic background, age). While big data integration of population genetics and single-cell omics studies can address variability between isolated cells and between individuals, one particularly important information dimension that is currently lacking is cell-cell interaction within the physiological context of a tissue.

    We will contribute to personalised and precision medicine research by comprehensively integrating single-cell and population genetics with spatial transcriptomics, a novel information dimension that is just beginning to be measured through recent advance in genomics technology. This approach aims to computationally reconstruct biological regulatory networks between genes and between cells, which underlie development (e.g. aging) and diseases (e.g. cancer). The systematic understanding of regulatory networks and biomarkers that are specific to individuals and cell types in physiological context will contribute to early disease diagnosis, targeted drug discovery and precision medicine.

    The research can lead to biomedical discovery by generating leading genomics and spatial imaging data, which are integrated by advanced computational deep learning methods. 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 receive a conducive training program 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)

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.

  • 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 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.

    Personalised and precision medicine require system genomics research to resolve variability at the cell, tissue and the organismal/inter-individual level (e.g. genetic background, age). While big data integration of population genetics and single-cell omics studies can address variability between isolated cells and between individuals, one particularly important information dimension that is currently lacking is cell-cell interaction within the physiological context of a tissue.

    We will contribute to personalised and precision medicine research by comprehensively integrating single-cell and population genetics with spatial transcriptomics, a novel information dimension that is just beginning to be measured through recent advance in genomics technology. This approach aims to computationally reconstruct biological regulatory networks between genes and between cells, which underlie development (e.g. aging) and diseases (e.g. cancer). The systematic understanding of regulatory networks and biomarkers that are specific to individuals and cell types in physiological context will contribute to early disease diagnosis, targeted drug discovery and precision medicine.

    The research can lead to biomedical discovery by generating leading genomics and spatial imaging data, which are integrated by advanced computational deep learning methods. 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 receive a conducive training program to develop a unique combination of multidisciplinary expertise in experimental biology, systems biology, biostatistics, and bioinformatics, and artificial intelligence.