Spatial transcriptomics enables the in situ mapping of gene expression, revolutionizing our ability to study tissue organization and cellular interactions. However, as this technology is increasingly adopted across biological and clinical research, many groups struggle with practical barriers to implementation, including platform selection, sample quality, sequencing depth, and experimental scalability. Here, we provide a comprehensive, practical guide to spatial transcriptomics, informed by the processing and analysis of over 1000 spatial samples across Visium, Visium HD, and Xenium platforms. We outline the best practices for experimental design, tissue handling, sequencing, and computational analysis, with special attention paid to clinical samples and high-throughput settings. Key lessons include the following: assemble a multidisciplinary team from the outset; prioritize, but do not overly rely on RNA quality metrics like DV200 and RIN, as meaningful data can be obtained from below‑threshold samples; avoid under‑sequencing; weigh gene panel size carefully, especially for imaging platforms like Xenium, since larger panels may dilute the per‑gene signal; and proactively prevent batch effects through randomization, replication, and detailed metadata tracking rather than relying solely on data analysis correction.

Our goal is to translate hands-on experience into actionable recommendations that support robust, reproducible spatial workflows. This guide is designed to assist researchers at all levels, from those designing their first spatial experiment to groups aiming to integrate ST into translational pipelines and large-scale studies.