Making proteomics reusable by design: interoperability standards for multi-omics and clinical translation

INTRODUCTION: Mass spectrometry-based proteomics has became a data-rich discipline, supported by public repositories, community standards, and increasingly scalable computational workflows. However, the public availability of proteomics datasets does not guarantee that they can be reanalyzed, compared with other studies, integrated with other omics layers, or used to support clinical interpretation. AREAS COVERED: This review discusses the differences between data repositories, the FAIR (Findable, Accessible, Interoperable, and Reusable) principles, interoperability, and true reusability, workflow-level reproducibility, multi-omics integration, and clinical phenotype harmonization. Common points of failure are highlighted: gaps in metadata, tool-specific outputs, misuse of missing values, batch-effect overcorrection, proteoform collapse, and premature claims of AI-readiness. EXPERT OPINION: Reusable-by-design proteomics requires that biospecimen context, metadata, spectral evidence, protein inference, quality control, workflow provenance, molecular specificity, and clinical meaning are all planned in concert from the outset of a study. The future impact of proteomics will depend not only on generating more data, but also on making those data independently interpretable, computable, auditable, and clinically reusable. For candidate medical tests, reusable-by-design should additionally anticipate metrological traceability, fit-for-purpose measurement uncertainty, and intended-use-specific analytical and clinical evidence so that clinically interpreted results remain comparable across laboratories, platforms, and time.