High-Resolution Bacterial Cytological Profiling Reveals Intrapopulation Morphological Variations upon Antibiotic Exposure.
Thanadon SamernateHtut Htut HtooJoseph SugieWarinthorn ChavasiriJoe PoglianoVorrapon ChaikeeratisakPoochit NonejuiePublished in: Antimicrobial agents and chemotherapy (2023)
Phenotypic heterogeneity is crucial to bacterial survival and could provide insights into the mechanism of action (MOA) of antibiotics, especially those with polypharmacological actions. Although phenotypic changes among individual cells could be detected by existing profiling methods, due to the data complexity, only population average data were commonly used, thereby overlooking the heterogeneity. In this study, we developed a high-resolution bacterial cytological profiling method that can capture morphological variations of bacteria upon antibiotic treatment. With an unprecedented single-cell resolution, this method classifies morphological changes of individual cells into known MOAs with an overall accuracy above 90%. We next showed that combinations of two antibiotics induce altered cell morphologies that are either unique or similar to that of an antibiotic in the combinations. With these combinatorial profiles, this method successfully revealed multiple cytological changes caused by a natural product-derived compound that, by itself, is inactive against Acinetobacter baumannii but synergistically exerts its multiple antibacterial activities in the presence of colistin. The findings have paved the way for future single-cell profiling in bacteria and have highlighted previously underappreciated intrapopulation variations caused by antibiotic perturbation.
Keyphrases
- single cell
- rna seq
- acinetobacter baumannii
- high resolution
- induced apoptosis
- multidrug resistant
- high throughput
- drug resistant
- cell cycle arrest
- pseudomonas aeruginosa
- escherichia coli
- fine needle aspiration
- endoplasmic reticulum stress
- electronic health record
- mass spectrometry
- big data
- cell death
- gram negative
- machine learning
- klebsiella pneumoniae
- high speed
- artificial intelligence
- mesenchymal stem cells
- cell proliferation
- pi k akt
- silver nanoparticles