Antibiotic exposure dynamically generates a substantial number of heterogeneous persisters along a spectrum of tolerance

Antibiotics are known to induce new persister cells during treatment, yet the inability to distinguish and quantify pre-existing versus drug-induced persisters has long obscured how antibiotics and genes shape persistence. Here, we develop a quantitative framework integrating kinetic modeling with serial-dilution time-kill (SDTK) assays to resolve persister population dynamics and accurately quantify both persister types. We show that antibiotic exposure dynamically generates a substantial number of persisters that are heterogeneous and distributed along a persistence spectrum. Across antibiotics, we uncover pronounced differences in rates of persister induction and elimination, with ampicillin inducing persisters at the highest rate and kanamycin at the lowest. Depending on dilution history, drug-induced persisters can dominate the persister pool. Our framework enables identification of genetic determinants specific to pre-existing and/or drug-induced persistence and reveals drug-dependent pre-existing persister fractions. Systematic sequential-drug treatments demonstrate that kanamycin persisters form the most tolerant subset, embedded within ciprofloxacin persisters that in turn are nested within the broader ampicillin persister subpopulation. Together, we propose a Drug-Induced Persistence-Spectrum (DIPS) model in which antibiotics differentially induce and select for persister subsets along a tolerance continuum.