Related Experiment Video
Updated: Jun 7, 2026
07:43
Methods to Enable Spatial Transcriptomics of Bone Tissues
Published on: May 3, 2024
Benchmarking spatial transcriptomics technologies with the multi-sample SpatialBenchVisium dataset
Mei R M Du1, Changqing Wang1,2, Charity W Law1,2
1The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.
Genome Biology
|March 29, 2025
Summary
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.
- 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.
