The epigenetic landscape of individual cells determines their identity and function, yet current methods for profiling chromatin modifications at single-cell resolution remain low-throughput, costly, or limited in parametric depth. Here we present EpiFlow, a spectral flow cytometry-based platform that enables the simultaneous quantification of 16 epigenetic markers, including histone post-translational modifications, DNA methylation, and hydroxymethylation, at the single-cell level. We demonstrate that EpiFlow is robust across species from yeast to mammals and resolves biologically meaningful epigenetic transitions during the cell cycle, stem cell differentiation, germinal centre B cell maturation, diabetic liver remodelling, and seizure-induced chromatin reprogramming. High-dimensional integration of EpiFlow data enables cell-type classification based solely on epigenetic profiles in liver, brain, blood, and cancer. Furthermore, EpiFlow detects on-target and off-target/indirect effects of epigenetic drugs in a high-throughput-compatible format. Collectively, these results establish EpiFlow as a broadly applicable platform for single-cell epigenetic analysis in basic, pharmaceutical, and translational research. ### Competing Interest Statement The authors have declared no competing interest. Agencia Estatal de Investigación, PID2022-137384OA-I00 (ERDF /EU), RYC2021-031713-I (European Union NextGenerationEU/PRTR), EUR2024-153539, CNS2024-154632 Fundación Ramón Areces, CIVP22A7609

“EpiFlow: multidimensional single-cell epigenetic profiling by spectral flow cytometry.”

**Abstract:** Predicting the spatio-temporal spread of zoonotic viruses poses a critical challenge for public health preparedness. This paper introduces a novel approach utilizing Recursive Bayesian Networks (RBNs) coupled with multi-modal data integration to dynamically model and forecast the risk of human-animal transmission. Our system, termed "EpiFlow," builds upon existing epidemiological models by incorporating real-time environmental data, animal population dynamics, and stochastic diffusion processes, allowing for significantly enhanced predictive accuracy and adaptability.