Imaging and spatial transcriptomics

The Genome Innovation Hub continually develops a number of protocols and procedures while undertaking our collaborative projects. Below are a selection of detailed developments useful to researchers undertaking genomic-related advanced imaging in the laboratory.

Developments

RNAscope® HiPlex assay using an automated slide scanner

RNAscope® Assay is the most advanced RNA in situ hybridization (ISH) assay based on ACD patented technology with signal amplification and simultaneous background noise suppression which advances RNA analysis in tissues and cells. Unique to this technology, RNAscope® delivers quantitative, sensitive and specific molecular detection of RNA species on a cell-by-cell basis with morphological context in a single assay. This enables researchers to visualize which genes are expressed, localize where they are expressed, and quantify the level of expression.

stLearn - a downstream analysis toolkit for spatial transcriptomics data

stLearn is designed to comprehensively analyse Spatial Transcriptomics (ST) data to investigate complex biological processes within an undissociated tissue. ST is emerging as the “next generation” of single-cell RNA sequencing because it adds spatial and morphological context to the transcriptional profile of cells in an intact tissue section. However, existing ST analysis methods typically use the captured spatial and/or morphological data as a visualisation tool rather than as informative features for model development. We have developed an analysis method that exploits all three data types: Spatial distance, tissue Morphology, and gene Expression measurements (SME) from ST data. This combinatorial approach allows us to more accurately model underlying tissue biology, and allows researchers to address key questions in three major research areas: cell type identification, cell trajectory reconstruction, and the study of cell-cell interactions within an undissociated tissue sample.

Featured projects	Duration
Developing a next generation single-cell transcriptomic tool Genome Innovation Hub Collaborative Project (UQ infrastructure)	2023
Long read spatial transcriptomics Genome Innovation Hub Collaborative Project (UQ infrastructure)	2023
Developing new "spatial omics" capabilities Genome Innovation Hub Collaborative Project (UQ infrastructure)	2021
Automated single cell quantitative in situ gene expression in cells and tissues (AutoMerFISH) Genome Innovation Hub Collaborative Project (UQ infrastructure)	2019
Spatial genomics technologies to study cancer and genetic diseases in tissue contexts Genome Innovation Hub Collaborative Project (UQ infrastructure)	2019

Publications & pre-prints

A Deep Learning Model for Molecular Label Transfer that Enables Cancer Cell Identification from Histopathology Images

Andrew Su, HoJoon Lee, Xiao Tan, Carlos J. Suarez, Noemi Andor, Quan Nguyen, Hanlee P. Ji

bioRxiv 2021.03.18.436004; doi: https://doi.org/10.1101/2021.03.18.436004

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

M Tran, S Yoon, ST Min, S Andersen, K Devitt, PY Lam, B Purdue, A Raghubar, SJ Hanson, K Jones, S Walters, ZK Tuong, HP Soyer, I Frazer, Q Nguyen

bioRxiv 2020.09.10.290833

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

Arti M. Raghubar, Duy T. Pham, Xiao Tan, Laura F. Grice, Joanna Crawford, Pui Yeng Lam, Stacey B. Andersen, Sohye Yoon, Monica S.Y. Ng, Siok Min Teoh, Samuel E. Holland, Anne Stewart, Leo Francis, Alexander N. Combes, Andrew J. Kassianos, Helen Healy, Quan Nguyen, Andrew J. Mallett

bioRxiv 2020.09.29.317917

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

Duy Pham, Xiao Tan, Jun Xu, Laura F. Grice, Pui Yeng Lam, Arti Raghubar, Jana Vukovic, Marc J. Ruitenberg, Quan Nguyen

bioRxiv 2020.05.31.125658;

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

Xiao Tan, Andrew Su, Minh Tran, Quan Nguyen, SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells

Bioinformatics, Volume 36, Issue 7, 1 April 2020, Pages 2293–2294,

Dr Hannah Leeson

Postdoctoral Research Fellow

Australian Institute for Bioengineering and Nanotechnology
A/Prof Andrew Mallett

Adjunct Senior Fellow

Institute for Molecular Bioscience
Associate Professor

Royal Brisbane Clinical Unit, Faculty of Medicine
Associate Professor Jessica Mar

ARC Future Fellow

Australian Institute for Bioengineering and Nanotechnology (AIBN)
Affiliate Research Fellow

Institute for Molecular Bioscience (IMB)
Dr Shyuan Ngo

Group Leader

Australian Institute for Bioengineering and Nanotechnology
Affiliate Senior Research Fellow

Queensland Brain Institute
Dr Quan Nguyen

Senior Research Fellow - Group Leader

Institute for Molecular Bioscience
Professor Ernst Wolvetang

Senior Group Leader

Australian Institute for Bioengineering and Nanotechnology
Dr Sohye Yoon

Research Specialist - Genomics

Genome innovation Hub