Single-Cell Technologies in Natural Product Pharmacology for Resolving Cellular Heterogeneity, Mechanism of Action, Resistance, and Context-Specific Therapeutic Response

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Natural products act within biologically heterogeneous systems in which pharmacological effects may differ among cell types, transient states, tissue niches, and resistant subpopulations. Population-averaged measurements can therefore obscure context-specific responses or incorrectly compress several cellular mechanisms into a single apparent effect. Single-cell and spatial technologies offer a route to resolve this heterogeneity, but their use in natural-product pharmacology remains methodologically uneven. We conducted a systematic mapping review of peer-reviewed literature published from 2017–2026 addressing single-cell technologies in natural-product pharmacology. Eligibility and charting distinguished treatment-exposed single-cell experiments from studies using pre-existing single-cell datasets to inform subsequent pharmacological investigation, and from methodological evidence required to interpret analytical validity. A verifiable candidate registry contained 54 records, of which 16 were excluded and 38 formed the mapped evidence base. Raw database-yield and full-text-stage denominators that could not be reconstructed reproducibly were not inferred. The map indicates a strong concentration of single-cell transcriptomic studies, particularly in cancer, inflammatory disease, traditional-medicine formulations, and immune-microenvironment research. Spatial, epigenomic, proteomic, and multimodal approaches remain less common. Cell-resolved studies frequently identify treatment-associated subpopulations or nominate mechanisms, whereas stronger mechanistic interpretation generally depends on orthogonal experimental validation. Direct evidence addressing resistance, persistence, temporal dynamics, dose dependence, and human treatment contexts is comparatively sparse. Single-cell technologies expand natural-product pharmacology from average response toward context-resolved pharmacological interpretation. Their principal value is analytical discrimination rather than automatic causal proof. Progress requires clearer separation of cell-state association, mechanistic inference, target validation, resistance biology, and translational evidence.