From Single Cells to Diagnosis: Proteomics Technologies in the Multi‐Omics Landscape of Rare and Mitochondrial Diseases

Abstract

Proteomics by mass spectrometry has rapidly matured from a niche method into a standard tool. The recent 10‐year trajectory of single‐cell proteomics has opened a new biological dimension for studying disease. Mitochondrial diseases, with their pronounced cell‐to‐cell heterogeneity, are particularly, well‐suited to these methods. Here, we discuss how this approach can serve as an orthogonal functional layer for rare disease diagnostics. We trace the evolution of rare disease diagnostics from biochemical enzyme assays through genomics, transcriptomics, proteomics and metabolomics, highlighting incremental gains in diagnostic yield from individual omics layers and their integration. We discuss the limitations of bulk approaches in capturing the functional consequences of genetic perturbations, the new opportunities opened up by single‐cell measurements and how spatial single‐cell proteomics can further enrich the biological signal of affected cells in diagnostic tissues. We observe that the persisting diagnostic gap reflects not only technological limitations but, increasingly, challenges in data sharing and infrastructure as well as interpretive frameworks for functional molecular evidence. In this context, we consider opportunities for artificial intelligence and the ethical dimensions of single‐cell proteomics in rare disease diagnostics. Finally, we propose a single‐cell deep visual proteomics (scDVP) framework for clinical diagnostics of rare diseases with cell‐to‐cell variability, arguing that mitochondrial diseases are an ideal proof‐of‐concept.