Single-Cell Nanomotion and Machine Learning for Parallel Bacterial Identification and Antibiotic Screening

Rapid and accurate identification of bacterial infections and their resistance to antibiotics is critical to effective clinical decision-making and combating antimicrobial resistance. However, current diagnostic approaches are typically segmented: techniques such as MALDI-TOF provide species identification, but cannot assess antibiotic susceptibility, while standard antimicrobial susceptibility (AST) tests are time-consuming and lack concurrent identification capability. In this study, we overcome these limitations by integrating single-cell nanomotion detection using graphene drums with machine learning (ML) algorithms to perform both tasks simultaneously within a single measurement. Nanomotion signals, nanoscale vibrations from single living cells, are recorded in real-time and transformed into time-frequency spectrograms, which serve as inputs to ML models trained for robust pattern recognition. Our framework enables the differentiation of Escherichia coli, Staphylococcus aureus , and Klebsiella pneumoniae , while simultaneously distinguishing resistant and susceptible strains with 98% precision. By coupling highly-sensitive graphene nanomotion sensors with advanced ML tools, our approach delivers a label-free bacterial diagnostics, offering both identification and susceptibility profiling at the single-cell level within a couple of hours.