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 a ramanome-based phage susceptibility test (RPST), a 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 h, 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.

Due to the limitations of existing approaches, a rapid, sensitive, accurate, comprehensive, and generally applicable strategy to diagnose and treat bacterial and fungal infections remains a major challenge. Here, based on the ramanome technology platform, we propose a culture‐free, one cell resolution, phenome‐genome‐combined strategy called single‐cell identification, viability and vitality tests and source tracking (SCIVVS). For each cell directly extracted from a clinical specimen, the fingerprint region of the D₂O‐probed single cell Raman spectrum (SCRS) enables species‐level identification based on a reference SCRS database of pathogen species, whereas the C‐D band accurately quantifies viability, metabolic vitality, phenotypic susceptibility to antimicrobials, and their intercellular heterogeneity. Moreover, to source track a cell, Raman‐activated cell sorting followed by sequencing or cultivation proceeds, producinging an indexed, high coverage genome assembly or a pure culture from precisely one pathogenic cell. Finally, an integrated SCIVVS workflow that features automated profiling and sorting of metabolic and morphological phenomes can complete the entire process in only a few hours. Because it resolves heterogeneity for both the metabolic phenome and genome, targets functions, can be automated, and is orders‐of‐magnitude faster while cost‐effective, SCIVVS is a new technological and data framework to diagnose and treat bacterial and fungal infections in various clinical and disease control settings.

Antimicrobial susceptibility tests (ASTs) are pivotal in combating multidrug resistant pathogens, yet they can be time-consuming, labor-intensive, and unstable. Using the AST of tigecycline for sepsis as the main model, here we establish an automated system of Clinical Antimicrobials Susceptibility Test Ramanometry (CAST-R), based on D₂O-probed Raman microspectroscopy. Featuring a liquid robot for sample pretreatment and a machine learning-based control scheme for data acquisition and quality control, the 3-h, automated CAST-R process accelerates AST by >10-fold, processes 96 paralleled antibiotic-exposure reactions, and produces high-quality Raman spectra. The Expedited Minimal Inhibitory Concentration via Metabolic Activity is proposed as a quantitative and broadly applicable parameter for metabolism-based AST, which shows 99% essential agreement and 93% categorical agreement with the broth microdilution method (BMD) when tested on 100 Acinetobacter baumannii isolates. Further tests on 26 clinically positive blood samples for eight antimicrobials, including tigecycline, meropenem, ceftazidime, ampicillin/sulbactam, oxacillin, clindamycin, vancomycin, and levofloxacin reveal 93% categorical agreement with BMD-based results. The automation, speed, reliability, and general applicability of CAST-R suggest its potential utility for guiding the clinical administration of antimicrobials.