Rapid detection of bacterial resistance to antibiotics using AFM cantilevers as nanomechanical sensors

Journal name:
Nature Nanotechnology
Year published:
Published online


The widespread misuse of drugs has increased the number of multiresistant bacteria1, and this means that tools that can rapidly detect and characterize bacterial response to antibiotics are much needed in the management of infections. Various techniques, such as the resazurin-reduction assays2, the mycobacterial growth indicator tube3 or polymerase chain reaction-based methods4, have been used to investigate bacterial metabolism and its response to drugs. However, many are relatively expensive or unable to distinguish between living and dead bacteria. Here we show that the fluctuations of highly sensitive atomic force microscope cantilevers can be used to detect low concentrations of bacteria, characterize their metabolism and quantitatively screen (within minutes) their response to antibiotics. We applied this methodology to Escherichia coli and Staphylococcus aureus, showing that live bacteria produced larger cantilever fluctuations than bacteria exposed to antibiotics. Our preliminary experiments suggest that the fluctuation is associated with bacterial metabolism.

At a glance


  1. Schematic representation of the set-up and the fluctuating cantilever.
    Figure 1: Schematic representation of the set-up and the fluctuating cantilever.

    a, Top: representation of the cantilever (C) after the attachment of living bacteria (B). Bottom: optical image of a cantilever, on which several adsorbed bacteria can be distinguished. The length of the cantilever is 205 μm. b, Top: representation of C in the acquisition chamber (A.C.), which is flushed by different liquids through the injection system (Inj.). The chamber is equipped with input (In) and output (Out) tubes for changing the media. Bottom: AFM illumination-detection system to measure the fluctuations of C. A laser beam (L) is focused on C. Its reflection is collected by a detector (D), allowing calculation of the sensor deflections. c, Depiction of the fluctuations of C produced by B adsorbed on its surface.

  2. Experiments involving the E. coli and S. aureus bacteria susceptible to ampicillin.
    Figure 2: Experiments involving the E. coli and S. aureus bacteria susceptible to ampicillin.

    a, Deflection of the sensor (top) and corresponding variance (bottom) for the E. coli experiment. The traces represent 20 s of recording for ‘PBS’ and 30 s for the other media. The time axis indicates the minute (starting from bacterial injection at ‘B’) when each recording was started. The ‘A’ line indicates when ampicillin was injected. The error bars represent the variation of the variance values in measurements performed in similar conditions. b, Corresponding results for the S. aureus experiment. Each trace represents 30 s. c, Normalized variances obtained when exposing the susceptible E. coli to different ampicillin concentrations (open circles) and the corresponding fit using a sigmoid function (red curve). The MIC and MBC values were obtained by intercepting the tangent at the inflection point (dashed line) with the 1 and 0 horizontal lines (Supplementary Section S4.1).

  3. Experiments describing the correlation between metabolism and fluctuations.
    Figure 3: Experiments describing the correlation between metabolism and fluctuations.

    a, Deflection of the sensor (top) and variance (bottom) for the ampicillin-resistant E. coli experiment. Each trace corresponds to 120 s. The time axis indicates the minute (starting from bacterial injection ‘B’) when each recording was started. The ‘A’ line indicates when ampicillin was injected. The error bars represent the variation of the variance values in measurements performed in similar conditions. b, Corresponding results from the experiment involving E. coli exposed to kanamycin and ampicillin. Each trace corresponds to 120 s. The lines indicate when kanamycin (K) and ampicillin (A) were injected. c, Deflection of the sensor (top) and variance (bottom) for the experiment involving E. coli exposed to glucose. Each trace represents 30 s. The measured variances are indicated over each corresponding column. The columns represent the variance normalized by the ‘PBS’ value.


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Author information


  1. Laboratoire de Physique de la Matière Vivante, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland

    • G. Longo,
    • L. Alonso-Sarduy,
    • J. Notz,
    • G. Dietler &
    • S. Kasas
  2. Service des Maladies Infectieuses, Département de Médecine, Centre Hospitalier Universitaire Vaudois (CHUV), CH-1011 Lausanne, Switzerland

    • L. Marques Rio &
    • A. Trampuz
  3. Institute de Microbiologie, Faculté de Biologie et de Médecine, Université de Lausanne, CH-1015 Lausanne, Switzerland

    • A. Bizzini
  4. Département des Neurosciences Fondamentales, Université de Lausanne, CH-1015 Lausanne, Switzerland

    • S. Kasas
  5. Present address: EPFL SB IPSB LPMV, BSP 414 (Cubotron UNIL), Rte de la Sorge, CH-1015 Lausanne, Switzerland

    • G. Longo


S.K., G.L., L.A.S. and G.D. designed the study. G.L., J.N. and L.A.S. performed the nanomotion sensor analyses and produced the LabVIEW software. G.L., L.A.S. and G.D. analysed the nanomotion data. G.L. and J.N. performed the glucose experiments. S.K. produced the finite elements model (FEM). G.D. performed the theoretical calculations. G.L. and S.K. collected and analysed the AFM and optical data. L.M.R. performed the MIC and MBC determination. A.B. and A.T. provided the bacteria. A.B., A.T. and L.M.R. provided the microbiological background. G.L. wrote the paper. All authors discussed the results and commented on the manuscript.

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