TYPE OF SUPPORT
Research Background
Single‑cell spatial technologies are an exciting technology that yields gene and/or protein expression data at the single cell level. For example, they allow researchers to visualise an immunofluorescence image of a cell and pinpoint the precise xy coordinates of transcripts within that cell.
These approaches have extensive applications for health research. For example, in cancer research, they enable the comparison of transcriptional profiles between immune cells infiltrating a tumour and those residing outside the tumour microenvironment.
Both scRNA‑seq and in situ spatial transcriptomics platforms such as CosMx, Xenium and MERSCOPE generate rich, multilayered datasets that can be explored from many analytical angles.
This resource grew out of hands‑on experience working with CosMx data produced for A/Prof Nic West and Dr Amanda Cox from the Griffith University Central Facility for Genomics, relating to their research projects in the mucosal immunology and cancer immunology spaces.
QCIF Role
This work involves refining tests that were originally developed for specific project needs and turning them into a clear, reusable how‑to format.
It also includes the creation of the Spatial Sampler resource and developing training that supports a wide range of users—from newcomers to spatial omics, to bioinformaticians who want to run specific tests without rewriting code, to trainers who can easily adapt open‑source instructions for their own workshops.
Research Outcome & Impact
The Spatial Sampler website forms the foundation of this work, supported by the development and delivery of an introductory spatial workshop created in partnership with Australian BioCommons and Sydney Informatics Hub, as well as a shorter “spatial taster” workshop first presented by QCIF at ABACBS, and offered again with Australian Biocommons.
These resources are already making an impact for the bioinformatics community:
For newcomers, they provide a gallery of what is possible along with worked examples that help researchers confidently begin their own analyses.
For experienced bioinformaticians, the site offers reusable code snippets that streamline routine tasks—saving time by allowing users to quickly retrieve and apply tested workflows to new projects.
For trainers, the processed dataset and accompanying analysis examples can be readily adapted for teaching. And for core facilities, the resource serves as a practical point of guidance for users who have just generated spatial data and need support understanding how to begin analysing it.
When I started working with spatial data, I could find tutorials for how to load, QC and annotate my datasets. But I struggled to find complete workflows for testing experimental questions. I wanted a collection of examples using real data - so that’s what Spatial Sampler aims to provide.
I had inspiration from amazing existing resources such as The R Graph Gallery. I’m now looking forward to others using Spatial Sampler and contributing more tools and resources that the community can use”
Sarah Williams, Bioinformatician
