Decoding the archipelago: single-cell biomarkers rechart the molecular geography of acute myeloid leukemia.

Acute myeloid leukemia (AML) is a molecularly archipelagic disease, characterized by profound cellular heterogeneity that fuels pathogenesis and therapy resistance. Traditional bulk sequencing approaches, by averaging signals across thousands of cells, have obscured the complex geography of leukemic stem cells (LSCs), dynamic clonal evolution, and critical ecosystem interactions within the bone marrow niche. This review explores how single-cell multi-omics technologies, including scRNA-seq, scDNA-seq, and high-dimensional cytometry, are fundamentally recharting the molecular landscape of AML. These approaches reveal that LSCs are not fixed entities but dynamic states shaped by genetic, epigenetic, and metabolic drivers. They decode the intricate cell-cell communication networks and immunosuppressive mechanisms that define the tumor microenvironment, uncovering novel vulnerabilities. Furthermore, single-cell profiling is revolutionizing the tracking of clonal dynamics in response to therapy, elucidating both genetic and non-genetic pathways to resistance and enabling a paradigm shift in measurable residual disease detection. By translating these high-resolution insights into novel biomarkers and therapeutic targets, single-cell technologies are paving the way for functional precision medicine in AML. In this review we explicitly (i) outline a practical roadmap to move single-cell candidate biomarkers from discovery through analytical and clinical validation to regulatory qualification, (ii) summarize evidence that single-cell assays are already used as correlative endpoints in clinical trials and indicate where they are being piloted as decision-enabling tools, and (iii) compare platform readiness (cost, standardization, turnaround and clinical trial feasibility) and the practical role of lower-dimensional assays for clinical validation. These translational perspectives aim to accelerate the responsible clinical adoption of single-cell biomarkers.