Reproducibility-driven clinical translation of spatial multi-omics in precision medicine

Abstract

Spatial multi-omics has emerged as a transformative approach for decoding tissue architecture by simultaneously profiling transcriptomic, proteomic, and epigenomic layers within their histological context. Yet despite rapid technological diversification, the transition from exploratory discovery to robust clinical deployment remains limited—not by measurement capability, but by the absence of systematic standards for data integration, reproducibility, and cross-platform comparability. Here, we present a comprehensive review of the reproducibility landscape in spatial multi-omics, organized around four interdependent challenges: defining the primary unit of spatial observation, establishing coordinate integrity across modalities, characterizing the strength of inter-modality coupling, and selecting computationally appropriate integration strategies. We survey the current technology landscape from high-definition sequencing arrays to multiplexed imaging and mass spectrometry-based proteomics, evaluating each platform’s standardization maturity alongside its measurement capabilities. We synthesize experimental design principles and quality control frameworks that are prerequisite for reproducible results, and review emerging computational methods for multimodal integration, tissue alignment, and spatial domain discovery. Finally, we consolidate these into a reporting checklist (organized along the UCCC axes) and a set of benchmarking principles that distinguish generic analytical best practice from the additional requirements specific to clinical translation in oncology, infectious disease, and pathology.