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Benchmarking spatial transcriptomics technologies with the multi-sample SpatialBenchVisium dataset.

Mei R M Du¹, Changqing Wang^1,2, Charity W Law^1,2

¹The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.

|March 29, 2025

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This summary is machine-generated.

This study introduces SpatialBenchVisium, a new reference dataset for spatial transcriptomics. It benchmarks the 10x Visium platform, showing improved data quality with probe-based methods and CytAssist for tissue preparation.

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Spatial transcriptomics enables gene expression measurement in tissue contexts.
The 10x Genomics Visium platform offers transcriptome-wide profiling of tissue sections.
Varied sample handling and library construction methods necessitate benchmarking for data quality assessment.

Purpose of the Study:

To present SpatialBenchVisium, a reference dataset for benchmarking the 10x Visium spatial transcriptomics platform.
To evaluate data quality across different tissue preparation protocols.
To assess the platform's ability to recover expected tissue features and biological signatures.

Main Methods:

Generation of a reference dataset from mouse spleen tissue under malaria infection.

Keywords:

10x Visium Benchmarking Differential expression Multi-sample analysis Spatial transcriptomics

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Inclusion of diverse tissue preparation protocols: fresh frozen (FF) and formalin-fixed paraffin-embedded (FFPE), with manual or CytAssist tissue placement.

Analysis of replicate samples for spatially variable gene detection, clustering, and cell deconvolution using matched single-cell RNA-sequencing (scRNA-seq) and public reference data.

Main Results:

Superior quality control metrics observed in samples prepared using probe-based capture methods, especially with CytAssist.
Validation of improved data quality with the Visium platform using CytAssist.
Successful identification of spleen cell types and tissue regions through clustering and deconvolution.
Recovery of known gene signatures related to biological sex and gene knockout via multi-sample differential expression analysis.

Conclusions:

The SpatialBenchVisium dataset provides a valuable resource for evaluating spatial transcriptomics data quality.
Probe-based capture methods, particularly with CytAssist, enhance data quality on the 10x Visium platform.
The benchmarking approach confirms the platform's utility in identifying biological features and signatures within complex tissues.