Single-cell morphological responses as predictors of ciprofloxacin resistance in Acinetobacter baumannii

The rise of antimicrobial resistance (AMR) coincides with a declining pipeline of new antibiotics, underscoring the urgent need for rapid and accurate antibiotic susceptibility testing (AST). Single-cell AST approaches offer the potential to shorten diagnostic turnaround times; however, reliably distinguishing antibiotic-sensitive from antibiotic-resistant strains at the single-cell level remains challenging. In this study, we employed bacterial cytological profiling (BCP), a single-cell imaging technique previously used for mechanism of action identification, to quantitatively analyze the morphological responses of ciprofloxacin (CIP)-sensitive and CIP-resistant Acinetobacter baumannii strains, including both laboratory and clinical isolates. Upon CIP treatment, resistant strains generally exhibited a significantly reduced degree of morphological change compared with sensitive strains, independent of their resistance level. We then leveraged these differences in the degree of morphological response to develop a phenotypic discrimination framework for future AST applications. Using the reference strain AB5075 and six clinical isolates, this approach successfully distinguished sensitive from resistant phenotypes in this pilot set of strains with an accuracy of 0.93. These findings highlight the potential of single-cell morphology-based analysis as a promising foundation for the development of next-generation, rapid AST platforms.

IMPORTANCE

Antimicrobial resistance represents a growing challenge for clinical management, in part because current antibiotic susceptibility testing can take days to determine which antibiotics will work. This study demonstrates that early, single-cell morphological responses to antibiotic exposure can serve as informative phenotypic indicators of resistance. By quantitatively analyzing how individual bacterial cells respond to antibiotic treatment, we show that resistant and susceptible strains can be reliably distinguished within 1 h of antibiotic exposure. Importantly, this approach remains effective across multidrug-resistant and clinical isolates. Together, these findings highlight rapid, imaging-based, single-cell phenotypic responses as a promising complement to current approaches for the future development of faster and more informative antimicrobial susceptibility diagnostics.