Evidence map›Paper›PMID 40156041›Full record

ArticleGenome biology2025

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

Mei R M Du, Changqing Wang, Charity W Law, Daniela Amann-Zalcenstein, Casey J A Anttila, Ling Ling, Peter F Hickey, Callum J Sargeant, Yunshun Chen, Lisa J Ioannidis and 6 more

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

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  7. Advances in Spatial Transcriptomics in Bone.Current osteoporosis reports · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Mei R M Du *The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.
Changqing Wang *The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.
Charity W Law *The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.
Daniela Amann-Zalcenstein *The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.
Casey J A AnttilaThe Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.
Ling LingThe Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.
Peter F HickeyThe Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.
Callum J SargeantThe Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.
Yunshun ChenThe Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.
Lisa J IoannidisThe Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.
Pradeep RajasekharThe Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.
Raymond K H YipThe Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.
Kelly L RogersThe Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.
Diana S HansenDepartment of Microbiology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, 3800, Australia.
Rory Bowden *The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.
Matthew E Ritchie *The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia. mritchie@wehi.edu.au.ORCID http://orcid.org/0000-0002-7383-0609

Funding

Chan Zuckerberg Initiative 2019-002443National Health and Medical Research Council GNT2017257
6 · The paper itself

Abstract

backgroundSpatial transcriptomics allows gene expression to be measured within complex tissue contexts. Among the array of spatial capture technologies available is 10x Genomics' Visium platform, a popular method which enables transcriptome-wide profiling of tissue sections. Visium offers a range of sample handling and library construction methods which introduces a need for benchmarking to compare data quality and assess how well the technology can recover expected tissue features and biological signatures.

resultsHere 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.

Indexed as

Gene Expression ProfilingTranscriptomeAnimalsBenchmarkingMalariaMiceSpleen10x VisiumBenchmarkingDifferential expressionMulti-sample analysisSpatial transcriptomics

Identifiers

PMID40156041
PMCPMC11954323

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.