Yes. MetwareBio provides 10x Genomics Visium HD spatial transcriptomics in addition to Stereo-seq. Our current probe-based Visium HD service is available for human and mouse tissue samples and enables high-resolution spatial gene expression profiling while preserving tissue morphology. Visium HD uses a continuous array of 2 × 2 μm spatially barcoded squares to localize gene expression signals within tissue sections.

Stereo-seq and 10x Genomics Visium HD are both sequencing-based spatial transcriptomics platforms, but they differ in RNA capture strategy, species compatibility, capture area, gene detection strategy, and native spatial resolution. Stereo-seq is particularly suitable for studies requiring broad species compatibility, large tissue coverage, and nanoscale spatial mapping, while MetwareBio’s current probe-based Visium HD workflow provides a standardized option for human and mouse spatial gene expression studies. Stereo-seq currently supports poly(A)-based capture for fresh-frozen tissues and a random-probe workflow for FFPE tissues.

Stereo-seq spatial transcriptomics supports both fresh-frozen and FFPE tissue samples. Fresh-frozen tissue blocks can be embedded in OCT or FSC 22 for cryosectioning, while FFPE samples can be submitted as properly prepared paraffin-embedded tissue blocks. Before spatial transcriptomics analysis, RNA quality and tissue morphology should be evaluated because sample integrity and section quality directly influence RNA capture and spatial gene expression data quality. Stereo-seq uses poly(A)-based RNA capture for fresh-frozen tissue and a random-probe strategy for its FFPE workflow.

Yes. Stereo-seq supports spatial transcriptomics analysis of plant tissues and is not limited to conventional human or mouse models. Its broad species compatibility makes it applicable to roots, stems, shoot apices, reproductive tissues, embryos, fruits, seeds, and other plant organs. Plant spatial transcriptomics can be used to investigate tissue organization, developmental regulation, environmental stress adaptation, pathogen interactions, and agriculturally important traits while preserving the spatial context of gene expression. STOmics describes its Stereo-seq transcriptomics workflows as species agnostic and applicable across animal and plant samples.

Stereo-seq provides a native nanoscale array resolution of 500 nm. The Stereo-seq array contains DNB capture features approximately 220 nm in diameter, with a 500 nm center-to-center distance between adjacent DNBs. This high-density spatial array supports cellular and subcellular spatial mapping of transcriptomic signals while maintaining broader tissue architecture. The effective biological resolution of an individual dataset can also depend on tissue morphology, RNA abundance, cell segmentation, sequencing depth, and downstream binning strategy.

The appropriate Stereo-seq chip size should be selected according to tissue dimensions and the anatomical region that needs to be captured within a continuous spatial field of view. Standard capture formats include 0.5 × 0.5 cm and 1 × 1 cm, while customized large-format arrays can be considered for larger organs, developmental specimens, or plant structures. Stereo-seq large-chip designs can extend up to 13 × 13 cm, allowing large tissue sections to be profiled without unnecessarily dividing biologically continuous regions.

Yes. Spatial transcriptomics can be integrated with single-cell RNA-seq and spatial metabolomics for multi-omics analysis of complex tissues. Matched single-cell RNA-seq data can strengthen cell-type annotation and support advanced analyses such as spatial cell-type mapping, cell–cell communication, spatial trajectory analysis, WGCNA, and inferred copy-number variation analysis. Integration with spatial metabolomics can further connect spatial gene expression patterns with localized metabolic phenotypes, providing complementary molecular information for investigating tissue heterogeneity, microenvironments, and spatial biological regulation.