Rapid and Quantitative Phage Susceptibility Test by Ramanome

Abstract Antimicrobial resistance poses an escalating global threat, renewing interest in bacteriophage therapy as a precision alternative to antibiotics. However, clinical translation remains hindered by the lack of rapid and quantitative phage susceptibility testing (PST) platforms capable of evaluating host range, infection potency, and effective multiplicity of infection (MOI). Here we present RPST, a ramanome-based phenotypic platform that captures infection-induced remodeling of bacterial macromolecular composition to unify these diagnostic requirements within a single workflow. RPST integrates four Raman biomarkers into a Composite Infection Index (CII), enabling rapid and lysis-independent discrimination between susceptible and resistant bacterial populations within ∼1 hour, with 96.0% categorical concordance (24/25) to plaque assays. As a continuous population-level metric, CII quantifies the proportion of infected cells, allowing quantitative ranking of phage potency against shared hosts. By resolving CII trajectories across the MOI and time, RPST further determines the minimal effective MOI, which is the lowest phage-to-bacterium ratio sustaining self-propagating infection, thereby defining the lower boundary for therapeutic feasibility. Together, these capabilities transform PST from static compatibility assays into a dynamic and quantitative framework that bridges in vitro infectivity assessment and infection dynamics relevant to phage therapy. Impact Statement Based on the rapid emergence of antimicrobial resistance, this study introduces RPST, a novel ramanome-based phage susceptibility testing platform. RPST detects phage-induced biochemical remodeling in bacteria within ∼1 hour, achieving 96.0% concordance with gold-standard plaque assays. By integrating four Raman biomarkers into a Composite Infection Index, it not only distinguishes susceptible from resistant strains but also quantifies phage potency and determines the minimal effective multiplicity of infection required for self-sustaining infection. This transformative approach moves beyond binary diagnostics to offer a dynamic, quantitative framework for precision phage therapy, significantly accelerating therapeutic decision-making and enhancing our ability to combat resistant infections. Competing Interest Statement Prof Jian Xu is among the founders of Single-Cell Biotech Co., Ltd. All other authors declare no competing interests. Data Availability All data and code supporting the findings of this study are publicly available in the Zenodo repository. (i) The raw ramanome dataset is available at: https://doi.org/10.5281/zenodo.17470426; (ii) The Python source code for the Composite Infection Index (CII) calculation is available at: https://doi.org/10.5281/zenodo.17470774.