Skip to content Skip to footer

Ables participant Systems biology and genomics lab, Harry Perkins Institute of Medical Research.

Project title

Analysing Spatial Transcriptome Data

Collaborators and funding

Contact(s)

Project description and aims

This project leverages the power of single-cell and spatial transcriptomics technologies to establish a comprehensive understanding of the intricate signalling landscapes tumours, in comparison to normal tissues. The key aims are:

  1. Characterize the landscape of autocrine and paracrine signalling in cancer cells across diverse tumour types and normal tissues.

  2. Develop novel bioinformatics tools and analytical pipelines for pre-processing, integrating, and analysing multi-dimensional spatial transcriptomics data.

  3. Delineate the spatial architecture of cancer cell signalling ecosystems and their impact on clinical outcomes by deciphering complex cell-to-cell communication networks.

  4. Quantify the likelihood of ligand-receptor interactions, map the spatial distribution of cancer subpopulations, and identify critical signalling axes promoting therapeutic resistance.

  5. Generate a comprehensive catalog of signalling pathways, a spatial atlas of cancer ecosystems, and propose potential therapeutic strategies targeting signalling vulnerabilities and microenvironmental modulation.

Through machine learning, network modelling, spatial statistics, and integrative multi-omics analysis, this program pushes boundaries in bioinformatics and precision oncology to improve outcomes for cancer patients.

How is ABLeS supporting this work?

ABLeS is providing critical computational resources and infrastructure to support the large-scale data analysis and bioinformatics efforts of this interdisciplinary program. Specifically:

  1. Service Units: to enable high-performance computing for processing and analysing the multi-dimensional single-cell and spatial transcriptomics datasets. These service units will power the computationally intensive tasks such as data integration, machine learning, network modelling, and spatial statistical analyses.

  2. Storage: To accommodate the massive data volumes generated by spatial transcriptomics technologies. This storage infrastructure will ensure secure and efficient data management, enabling seamless access and processing of the multi-omics datasets.

  3. Software Stack: Through its comprehensive software stack, ABLeS offers access to cutting-edge bioinformatics tools, libraries, and frameworks essential for spatial data analysis, such as nextflow. This software ecosystem empowers the development of novel analytical pipelines.

By providing these critical computational resources, storage infrastructure, and software ecosystem, ABLeS plays a pivotal role in enabling the success of this pioneering research program, accelerating discoveries in spatial cancer signalling and precision oncology.

This work is supported through Production bioinformatics scheme provided by ABLeS. The supports includes unlimited temporary storage on scratch, 1 TB permenant storage and 50 KSUs per quarter.

Expected outputs enabled by participation in ABLeS

Novel bioinformatics tools and analytical pipelines for spatial transcriptomics data - These will be open-source and published/shared through appropriate code repositories like GitHub.

Integrated single-cell/spatial transcriptomics datasets with clinical information - These multi-omics datasets will be deposited in public repositories like the Human Cell Atlas (HCA) and Human Tumor Atlas Network (HTAN).

Comprehensive atlas of spatial signalling ecosystems across cancer types and normal tissues - This will be published as an interactive web resource and the data will be made available through public repositories.

Knowledgebase of autocrine/paracrine signalling pathways in tumour and normal tissues - This will be published as a database resource and made publicly accessible.

Quantitative models of ligand-receptor interactions and signalling crosstalk - These computational models will be described in published manuscripts and the code/data will be shared via repositories.


These details have been provided by project members at project initiation. For more information on the project, please consult the contact(s) or project links above.