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Methods to Enable Spatial Transcriptomics of Bone Tissues
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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
PubMed
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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 VisiumBenchmarkingDifferential expressionMulti-sample analysisSpatial 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.