From single-cell maps to diagnostics: enabling biomarker discovery in precision medicine

Single-cell omics technologies are transforming the landscape of biomarker discovery by offering unprecedented insights into the cellular and molecular complexity of health and disease. Unlike bulk profiling approaches that obscure subtle but critical signals through averaging, single-cell platforms capture distinct cell states, rare subpopulations, and transitional dynamics that are essential for precision diagnostics. This review presents strategies for translating single-cell data into clinically actionable biomarkers by leveraging advances in transcriptomic, proteomic, epigenomic, and spatial profiling. In addition, emerging perturbation-based approaches and lineage-informed datasets are enabling deeper mechanistic insights into disease processes. Artificial intelligence, particularly foundation models and stability-driven feature selection, now allows these complex datasets to be interpreted in ways that prioritize robustness and clinical relevance. By linking high-resolution cellular mapping to real-world diagnostic applications, single-cell omics is redefining how biomarkers are discovered, validated, and implemented in precision medicine.