Single-cell multi-omics technologies have emerged as transformative tools in biomarker discovery, enabling the concurrent interrogation of multiple molecular layers such as the genome, epigenome, transcriptome, proteome and metabolome at single-cell resolution. These approaches circumvent the limitations of bulk profiling by capturing cellular heterogeneity, lineage dynamics and rare cell populations that are critical for understanding disease pathophysiology and therapeutic response. This review systematically examines recent innovations in single-cell multi-omic platforms, including combinatorial approaches such as scRNA-seq with ATAC-seq, CITE-seq, spatial transcriptomics, and emerging single-cell metabolomics methodologies. Applications in oncology, neurodegeneration, immunopathology, and infectious disease are highlighted, with a focus on how multi-modal single-cell data is reshaping the biomarker landscape for early detection, patient stratification, and monitoring of minimal residual disease. We further explore key bioinformatics challenges, including Batch effects, missing modalities, high-dimensional data integration, noise reduction, and trajectory inference, as well as translational barriers related to validation, reproducibility, and regulatory frameworks. Advances in machine learning, microfluidic engineering, and spatial omics integration are also discussed as enablers of clinical-grade biomarker pipelines. This synthesis underscores the pivotal role of single-cell multi-omics in accelerating precision medicine and underscores critical areas for future research and standardization. • Single-cell multi-omics technologies and their integration strategies for high-resolution biomarker discovery. • Disease-specific applications across oncology, neurodegeneration, infectious, autoimmune, and metabolic disorders. • Validation strategies, regulatory considerations, and clinical trial integration of single-cell biomarkers.
Sahu et al. (2026) studied this question.