Benchmarking spatial transcriptomics technologies with the multi-sample SpatialBenchVisium dataset

Here we present SpatialBenchVisium, a unique reference dataset generated from spleen tissue of mice responding to malaria infection spanning several tissue preparation protocols (both fresh frozen and FFPE, with either manual or CytAssist tissue placement). We note better quality control metrics in reference samples prepared using probe-based capture methods, particularly those processed with CytAssist, validating the improvement in data quality produced with the platform. Our analysis of replicate samples extends to explore spatially variable gene detection, the outcomes of clustering and cell deconvolution using matched single-cell RNA-sequencing data and publicly available reference data to identify cell types and tissue regions expected in the spleen. Multi-sample differential expression analysis recovered known gene signatures related to biological sex or gene knockout.

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Publication Details

Journal
Genome biology
Published
2025-03-28
DOI
https://doi.org/10.1186/s13059-025-03543-4
Citations
15
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
12.09

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article

Benchmarking spatial transcriptomics technologies with the multi-sample SpatialBenchVisium dataset

Charity W. Law, Daniela Amann‐Zalcenstein, Matthew E. Ritchie, Callum J. Sargeant et al.
15 citations
Genome biology
Single-cell and spatial transcriptomics
12.09
article

Benchmarking spatial transcriptomics technologies with the multi-sample SpatialBenchVisium dataset

Charity W. Law, Daniela Amann‐Zalcenstein, Matthew E. Ritchie, Callum J. Sargeant, Lisa J. Ioannidis, Ling Ling, Casey J. A. Anttila, Diana S. Hansen, Pradeep Rajasekhar, Kelly L. Rogers, Yunshun Chen, Rory J. Bowden, Raymond K. H. Yip, Peter F. Hickey, Changqing Wang, Mei R. M. Du
article en
15 citations

Abstract

Here we present SpatialBenchVisium, a unique reference dataset generated from spleen tissue of mice responding to malaria infection spanning several tissue preparation protocols (both fresh frozen and FFPE, with either manual or CytAssist tissue placement). We note better quality control metrics in reference samples prepared using probe-based capture methods, particularly those processed with CytAssist, validating the improvement in data quality produced with the platform. Our analysis of replicate samples extends to explore spatially variable gene detection, the outcomes of clustering and cell deconvolution using matched single-cell RNA-sequencing data and publicly available reference data to identify cell types and tissue regions expected in the spleen. Multi-sample differential expression analysis recovered known gene signatures related to biological sex or gene knockout.

Genome biologyVol. 26(1)
The University of Melbourne (AU), Walter and Eliza Hall Institute of Medical Research (AU), Australian Regenerative Medicine Institute (AU), Monash University (AU)
Chan Zuckerberg Initiative, National Health and Medical Research Council
Openalex Percentile: Top 1%
Single-cell and spatial transcriptomics
12.09
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