Benchmarking spatial transcriptomics technologies with the multi-sample SpatialBenchVisium dataset

Benchmarking spatial transcriptomics technologies with the multi-sample SpatialBenchVisium dataset

Abstract Background Spatial 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...

Full description

Saved in:
Bibliographic Details
Main Authors: 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, Pradeep Rajasekhar, Raymond K. H. Yip, Kelly L. Rogers, Diana S. Hansen, Rory Bowden, Matthew E. Ritchie
Format: Article
Language:English
Published: BMC 2025-03-01
Series:Genome Biology
Subjects:
Spatial transcriptomics
Benchmarking
10x Visium
Multi-sample analysis
Differential expression
Online Access:https://doi.org/10.1186/s13059-025-03543-4
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Abstract Background Spatial 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. Results 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.
ISSN:1474-760X

Similar Items