Ten quick tips for spatial transcriptomics analysis

Spatial transcriptomics (ST) enables genome-wide gene expression profiling while retaining spatial context within tissue sections. Since the foundational work by Ståhl et al. in 2016, the field has expanded rapidly, with diverse platforms now spanning sequencing-based (e.g., Visium, Visium HD, Slide-seq, Stereo-seq, and Seq-Scope) and imaging-based (e.g., MERFISH, Xenium, and CosMx SMI) approaches. The breadth of platforms, data structures, and computational tools, however, can be daunting for newcomers. Here, we present ten quick tips spanning the entire ST research workflow: whether ST suits a given biological question, how to select a platform aligned with study objectives, how to understand and process ST data, and which software tools to employ for analysis and visualization. We further discuss interpreting spatial patterns in biological context, integrating complementary modalities such as single-cell RNA sequencing and spatial proteomics, and leveraging public datasets and sharing results. Finally, we highlight current limitations of ST, particularly the challenge of reconstructing three-dimensional tissue architecture from serial tissue sections. This review provides biologists, bioinformaticians, and clinician-scientists with a concise, platform-neutral roadmap for incorporating ST into research, from experimental design to biological discovery.