Transformative advances in single-cell omics: a comprehensive review of foundation models, multimodal integration and computational ecosystems

Recent advances in single-cell multi-omics technologies have revolutionized cellular analysis, enabling comprehensive exploration of cellular heterogeneity, developmental trajectories, and disease mechanisms at unprecedented resolution. Foundation models, originally developed for natural language processing, are now driving transformative approaches to high-dimensional, multimodal single-cell data analysis. Frameworks such as scGPT and scPlantFormer excel in cross-species cell annotation, in silico perturbation modeling, and gene regulatory network inference. Multimodal integration approaches, including pathology-aligned embeddings and tensor-based fusion, harmonize transcriptomic, epigenomic, proteomic, and spatial imaging data to delineate multilayered regulatory networks across biological scales. Federated computational platforms facilitate decentralized data analysis and standardized, reproducible workflows, fostering global collaboration. Challenges persist, including technical variability across platforms, limited model interpretability, and gaps in translating computational insights into clinical applications. Overcoming these hurdles demands standardized benchmarking, multimodal knowledge graphs, and collaborative frameworks that integrate artificial intelligence with human expertise. This review synthesizes recent technological advancements and proposes actionable strategies to bridge single-cell multi-omics innovations with mechanistic biology and precision medicine.