RNAscope HiPlex AssayAim

The general aim of the proposed project is to develop an innovative spatial technology with a complete pipeline from wet-lab experiments to dry-lab analysis to study tissue biology of cancer and genetic diseases. Most genomics technologies require disassociation of cells from original tissues, thereby discarding the physiological spatial information. The spatial technology allows to quantitatively assess tissue heterogeneity in cancer and genetic underpinnings of disease at tissue and single-cell level without dissociating the tissue. With both spatial image and genomics information, the technology allows to develop innovative software tools for combining image and transcriptional genomics data that will eventually be useful for computer-aided tissue diagnosis.

Brief project outline

Combining gene expression data with the location and physiological context of the cells in a tissue, we will determine cell types, groups of cells spatially clustered into microenvironments, and molecular interactions between cell types. To integrate genomics (microscopic) and imaging (macroscopic) data for systematically assessing diseased tissues, we will develop an innovative analysis approach using a convolutional neural network framework, which is especially suitable for spatial data, and fast parallel computation to train multiple layers of features in image. Through this project, we aim to evaluate the use of spatial transcriptomics together with machine learning to better define the molecular nature of the tissue heterogeneity in cancer and genetic diseases. We expect that spatial transcriptomics will bring the world-class technology that can serve the broad genomics research projects at UQ and beyond.

Genomics-based innovative aspect of proposal

We aim to develop spatial transcriptomics (ST) to become a major new capability that will help UQ to establish a pioneering nationally and competitive internationally position in genomics technology. Current genomics assays are limited by: 1) the need to dissociate cells from native tissues, thus discarding the in vivo spatial context, 2) the lack of resolution to measure at the single-cell level, and 3) the number of genes that can be measured in situ. In a world-first, we will combine the state-of-the-art ST together with the rapid advance in artificial intelligence analytics. For complex, multimodal data analysis, we develop an innovative approach to integrate genomics (microscopic) and imaging (macroscopic). The approach therefore integrates methods from a variety of experimental and data analytics fields using cutting-edge genomics technology and advanced computational analysis that not only allow UQ to stay at the spearhead of dry and wet genomics research.

Broad applicability of the technique

We expect that spatial transcriptomics will bring the world-class technology that can serve the broad genomics research projects at UQ and beyond. We expect that the technology can be transferred to IMB sequencing facility as a complete pipeline from sequencing to data analysis, thereby making it widely available and implementable to UQ researchers. Users only need to submit fresh-frozen tissues or OCT embedded tissues. The data can be readily incorporated with other genomics data.

Developments arising from this project

GIH development: RNAscope® HiPlex assay using an automated slide scanner

Bioinformatic tool: stLearn - a downstream analysis toolkit

Publications arising from this project

Pre-print: Spatially resolved transcriptome profiles of mammalian kidneys illustrate the molecular complexity of functional nephron segments, cell-to-cell interactions and genetic variants

Pre-print: Spatial analysis of ligand-receptor interactions in skin cancer at genome-wide and single-cell resolution.

Pre-print: stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues.

Publication: SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells.

Project members


Research collaborators

Dr Quan Nguyen

Dr Quan Nguyen

Senior Research Fellow - Group Leader
Institute for Molecular Bioscience
A/Prof Andrew Mallett

A/Prof Andrew Mallett

Adjunct Senior Fellow
Institute for Molecular Bioscience
Associate Professor
Royal Brisbane Clinical Unit, Faculty of Medicine
Arti Raghubar

Arti Raghubar

PhD candidate
University of Queensland

Genome Innovation Hub

Stacey Andersen

Stacey Andersen

Senior Research Assistant
Genome Innovation Hub
Senior Research Assistant
IMB Sequencing Facility
Dr Sohye Yoon

Dr Sohye Yoon

Research Assistant
Genome innovation Hub
Jun Xu

Jun Xu

Research Assistant
Genome Innovation Hub
Dr Brooke Purdue

Dr Brooke Purdue

Operations Manager
Genome Innovation Hub